Strengths and limitations of applying a diet quality index

DQIs are useful tools for assessing changes in overall patterns of food choices, and in

this regard, provides useful insights for the public health domain. Diet quality scores

provide quick and easily interpretable means of ranking standards of overall diet quality

in relation to dietary guidelines, which are developed based on scientific evidence

arising from studies investigating diet-disease prevention [92]. Effectively, diet quality

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mortality and disease risk [173, 310]. This supports evaluations into the effectiveness

of dietary guidelines in chronic disease prevention and further inform strategies for

nutrition intervention programs [296].

However, several factors influence the reliability and robustness of DQIs particularly

relating to methodological design challenges. The process of designing a DQI involves

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Table 3.2. Description of three main variables required for designing a DQI.

Variable Aim of variable Potential approaches for including variable

Dietary components This relates to diet-related variables

such as food groups (e.g. milk and

dairy products) and/or nutrients (e.g.

saturated fat), of foods (e.g. legumes)

for the index.

As most indexes are designed to explore associations between dietary

habits of populations and health outcomes, dietary components

deemed to provide health protective effects are usually included as

complimentary of national dietary guidelines, e.g. ADG/AHGE [10]

or United States Dietary Guidelines [308]. Foods considered

detrimental to health (i.e. discretionary or ‘junk foods’ are also

usually included, however, as recommendations on limiting

consumption of these foods are incorporated in dietary guidelines.

Consumption thresholds/

cut-off values

A consumption threshold, or method

to quantify components included in

the index to enable a scoring system

to be developed.

Consumption thresholds may be guided by group medians [316] or

group quintiles [317] in population studies. Alternatively, index

components may also be scaled based on levels of consumption

recommended in dietary guidelines [318], e.g. aiming for a minimum

of 2 serves of fruit per day.

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quality. most straightforward methods. For the latter, scores of “1” may be

allocated if consumption meets or exceeds cut-off values while “0” is

awarded if consumption falls below cut-offs, which may be reversed

depending on the purpose of the DQI. A more common method is to

utilise a scaled scoring system which allows greater range of scores to

be obtained [322], e.g. 0 serves = 0 points, 1 serve = 1 point, 2 serves

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Further to methodological challenges, the ability of DQIs in predicting health

outcomes in relation to dietary guidelines will be influenced by the design of the tool

in capturing the relevant data [304]. For example, nutrient and food or food-group

based dietary indices pose an advantage over those developed based on food or food-

groups or nutrients alone, as the former approach captures intricacies of food intake

patterns, as well as both nutrient and non-nutrient components within diets [298,

301]. Analyses of mixed dishes, such as a stir-fry, also prove challenging when DQI

components are based on individual foods or food groups. Reference to publicly

accessible food databases is recommended to support accurate categorisation of food

for analysis.

Furthermore, although most existing DQIs are able to predict mortality or link

disease risks to diet, reported associations between these and diet quality score

outcomes are deemed modest [173]. Considerable differences found in components

included in different DQIs as well as between recommendations provided in country-

specific dietary guidelines have, therefore, called into question the usefulness of

these tools as health outcome predictors [1, 101]. The inherent complexities and

heterogeneous nature underlying DQIs as primary tools used in measuring diet

quality must also be acknowledged. Accuracy of findings are limited by the

appropriateness of the DQI selected [323], and is only as robust as the components

included in the index. Additionally, although diet quality scores are easy to compute

and provide an overall standard of diet quality, scores are not descriptive of food

intake patterns [92]. Care must, therefore, be taken when translating outcomes into

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Despite these limitations, DQIs are, to date, the most relevant, valid and effective

holistic tools that enable links between dietary patterns and health to be established

[173, 306, 324]. Evidentiary support for utilising DQIs is conditional, however,

upon acknowledging limitations of these tools when interpreting outcomes. Several

recommended strategies should also be considered when applying these tools. First

and foremost, appropriate DQIs must be selected, guided by the study aim [1, 325].

For example, is the purpose of applying a DQI to measure adherence to a country

specific dietary guideline, or to a dietary pattern such as the Mediterranean style of

diet? While both require food-based DQIs, components included in the index will

inevitably vary. Consideration should also be given towards how scores may change

over time. Repeating measurements to compare scores in relation to changes in

dietary patterns is consequently recommended at different time points, as an

assessment of the current evidence on population health and the effectiveness of

nutrition interventions [310, 323, 326].

As DQIs are methodologically challenging to develop, validating DQIs are also

recommended to improve the reliability of these tools [92]. In the literature, three

attributes for validating DQIs have been proposed, based upon criteria used to

evaluate the HEI-2005 [307]. Descriptions and examples of the three criteria

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Table 3.3: Proposed criteria for validating diet quality indexes.

Criteria Description of criteria Example of validation

Content validity A qualitative examination to ensure

relevant aspects of the dietary

guidelines are included in the index.

Recommendations to increase whole fruit and vegetables intakes in the

2010 Dietary Guidelines for Americans [308] was scored based on the

proportion of foods consumed from represented by components such as

“Total fruit” and “Total vegetables” in the HEI-2010 [306].

Construct validity A quantitative examination to assess

if the index can measure what it has

been intended for, i.e. diet quality.

The construct validity of a Food Choices Score (FCS) [118] was

ascertained by applying the FCS against idealised theoretical dietary

models representative of high diet quality, to assess if maximum scores

were achievable.

Reliability Examines the relationship between

variables in the index and identifies

if some variables are more

influential than others.

Utilise statistical procedures such as PCA or Cronbach’s coefficient α [322] to assessing the component of the index.

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