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Healthy eating guides: Time for a rethink?

2007· article· en· W2061239787 on OpenAlexaboutno aff
Katrine I. Baghurst

Bibliographic record

VenueNutrition & Dietetics · 2007
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaFood groupConsumption (sociology)GeographyEnvironmental healthMedicinePublic healthPopulationSociologySocial science

Abstract

fetched live from OpenAlex

In most countries, the ‘public face’ of the national nutrient intake recommendations takes the form of a food choice or ‘healthy eating’ guide. Most countries base their guide on food groups but the number of food groups varies widely from as few as three, in countries such as Fiji and a number of African countries, to seven or eight in countries such as the Caribbean. Industrialised countries, generally use between four and six food groups. The graphic display of food group recommendations also varies from country to country, from pyramids/triangles (e.g. USA, Nordic countries and some health groups in China, Australia and New Zealand) to a plate or circular/semicircular design (UK, Australia, many European countries and the Caribbean). Other designs include a food square (Iran), a quarter rainbow (Canada), steps (New Zealand), or a traffic light (UK Health Education Council). Australia's current guide, the Australian Guide to Healthy Eating (AGHE),1 is based on the National Health and Medical Research Council's 1994 Core Food Group2 analysis, which translates nutrient requirements to food consumption recommendations. This, in turn, was based on both the 1991 Recommended Dietary Intakes (RDIs)3 and Australian 1989–1990 per capita food availability data.4 The AGHE also took into account the Dietary Guidelines for Australians (1992)5 and Children and Adolescents (1995).6 Since the AGHE was developed, a new set of Dietary Guidelines7–9 and a new set of Nutrient Reference Values10 (NRVs) have been published with a wider range of nutrients and more values per nutrient. The NRVs include both recommendations to ensure adequacy of nutrient intake and, for some nutrients, recommendations about levels that may reduce chronic disease risk. The AGHE is therefore in need of revision but what, in particular, needs to be taken into account? We now have additional recommendations for nutrients such as n-3 and long-chain n-3 fatty acids, n-6 fatty acids, vitamin D, dietary fibre, water, pantothenic acid, biotin, choline, vitamin K, chromium, copper, fluoride, manganese and molybdenum. However, our current food databases are limited so our ability to model diets that will be adequate in all of these is limited. The paper by Shrapnel and Baghurst in this issue addresses some of the implications for the AGHE of the new n-3, LCn-3, n-6 and vitamin D recommendations (as well as vitamin E) in relation to placement of unsaturated fats and oils in the current guide where, for the most part, they are placed in the ‘extras’ or ‘non-core’ food category.11 They conclude that these foods are a valuable source of these particular nutrients and also comment that there may be a case for aligning the unsaturated fats and oils with other related food categories such as nuts and seeds. Currently, nuts and seeds are placed in the ‘meats and alternatives’ category with animal protein foods such as red meats, poultry, fish, eggs and plant-based protein foods such as beans, lentils or chickpeas. Their placement here is primarily to offer an alternative non-meat source of protein. The guide itself states that the major nutrient contributions of this ‘meats and alternatives’ group are protein, iron, niacin and B12. They also are a major contributor to zinc. However, plant-based foods provide no vitamin B12 and the iron and zinc they contain are much less bioavailable than from animal protein sources. This latter issue has been highlighted in the new NRVs where it is recognised that the amount of iron in vegetarian diets needs to be some 80% higher, and that of zinc 50% higher, than that in a mixed western diet to achieve the same intake. This begs the question, do we need a separate guide designed specifically for vegetarians that caters to their specific requirements in a much more flexible way or do we attempt to retain a compromise ‘one size fits all’ guide that may simplify, but not optimise, the message. Apart from a range of new nutrients, recommendations for other such as folate, which has doubled since the 1991 review, have changed substantially. Another issue to be addressed is what level of intake should be used as the benchmark for a given age/gender. The baseline diets for the current AGHE (e.g. 5000 kJ for adults) were modelled to achieve 70% of RDI requirements for nutrients but they accounted for only half the daily energy needs of the average adult, and insufficient fibre. The remaining energy/fibre needs were accounted for in two alternative ways either by increasing cereal serves (e.g. to 6–12 for adult men) and introducing an ‘extras’ allowance or by increasing serves across all food categories. The 70% RDI benchmark figure was chosen at the time to reflect the fact that the RDI itself is a value set to cover the needs of nearly all people in an age/gender category and is therefore set too high for most people. In a country where overconsumption of certain foods is as big a problem as underconsumption of others, this is a justifiable approach. However, with the advent of the new NRVs, we have nutrient-specific estimates of average requirements for various nutrients by age/gender called the estimated average requirements. We also now have adequate intake figures for some additional nutrients where the available data are insufficient to set an estimated average requirement and RDI. There is a need, therefore, to re-benchmark and remodel the nutrient requirement basis of the food choice guide. There are other issues that will need to be addressed. For example, how can we best use modern methods of communication to make the guide widely available and responsive not only to age, gender and activity patterns but perhaps alsoto food preferences, be they culturally based or specific to food/food groups. The current US MyPyramid Guide provides a web-based program that allows people to tailor their diets according to age, gender and activity patterns and offers choices within food groups but does not allow for specific food preferences across food categories. Can we design a user-friendly system that allows us to design a ‘meat-lovers’ diet or a ‘broccoli-haters’ diet that will conform to recommendations? Preliminary analysis undertaken during development of the NRVs indicates that this may be so. However, what degree of flexibility can we build in without confusing the message? We may still need an ‘average’ recommendation for paper/pencil/poster presentations but food choice flexibility within this overall concept may make the recommendations more widely acceptable. In addition, do we need to revisit the food groupings? Apart from the issue of the ‘meats and alternatives’ and ‘fats/oils/nuts/seeds’ issues discussed earlier, do we need to further refine the vegetable or fruit categories to highlight the special attributes of certain types? If we leave vegetables as one category, do we exclude potatoes and some other starchy tubers as some countries do and place them elsewhere? Do we need to revisit the concept of what constitutes an ‘extra’ food—are hot chips containing only 1–2% unsaturated fat ‘extras’ or ‘vegetables’? What is the place of foods categories such as herbs and spices—are they just flavourings that can also help reduce salt and fat use, do we just subsume them in fruits and vegetable groupings or do their particularly high phytochemical content and concentrated nutrient profile justify a specific role for them in a revised guide? In short, there are a number of issues to be addressed in updating the current AGHE and to optimise its usefulness and uptake. Input will be needed from the beginning of the process from a wide range of health professionals as well as end-users such as consumers and food industry.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.473
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

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

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 head0.040
GPT teacher head0.344
Teacher spread0.304 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2007
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