Energy and nutrient intake

Nutrient intakes are derived from amounts of food, beverage and/or supplement reported as consumed in the study using the AUSNUT 2023 files. Nutrient intakes are reported to enable:

• comparison against Nutrient Reference Values (NRVs), the 2013 Australian Dietary Guidelines (ADG), national alcohol drinking guidelines
• monitoring of the mandatory addition of nutrients to food in Australia under the Australia New Zealand Food Standards Code
• comparison to previous national nutrition surveys (where feasible).

To promote meaningful comparisons with NRVs and the ADG, dietary intakes of nutrients are only reported where the food or supplement is the major source and available information is of sufficient quality on amounts of the nutrient in foods. 

Information on nutrient intakes from food and dietary supplements is available for the nutrients listed in the Nutrients table on the Downloads page.

One aspect of nutrient intake is that of a person's macronutrient intake compared to their total daily energy intake referred to as “Macronutrient balance” (NHMRC 2006). Unlike the micronutrients, macronutrients (proteins, fats and carbohydrates) all contribute to dietary energy intake. Alcohol can also contribute to dietary energy. The acceptable macronutrient distribution range (AMDR) represents the range of intake for each macronutrient, expressed as a percentage of total energy, that is associated with reduced risk of chronic disease and adequate intake of other essential nutrients.

The proportion of total energy contributed by macronutrients can be compared to the AMDRs. To compare survey results to established AMDRs, the percentage contribution to total energy from each macronutrient was calculated using standard energy conversion factors, see table below. Dietary supplements were assumed to contain zero energy and were excluded from the calculations.

Some respondents may not have consumed any foods or beverages during the recall period, or that the foods and beverages they did consume were calculated to contain zero kilojoules of energy. Respondents with an energy intake of zero kilojoules on day the prior to interview (Day 1) were retained in totals for macronutrient contributions. For these respondents, macronutrient contributions were set to 0.

Basal metabolic rates (BMR) are the amount of energy needed for a minimal set of functions necessary for life over a defined period. They are sufficient only for the functioning of the vital organs such as the heart, lungs, nervous system, kidneys, liver, intestine, sex organs, muscles, and skin. 

A BMR was calculated for each respondent and expressed as kilojoules (kJ) per 24 hours using age, sex, weight (kg) and height (cm). There was no adjustment for activity levels or health status. Where measured weight or height was not available from a respondent, this was estimated using imputation in both surveys. This is a change to the study since 2011–13 and should be considered when interpreting BMR results over time. Further information about the imputation process is available under Physical Measures in each survey’s methodology.

The ratio of energy intake to basal metabolic rate (EI:BMR ratio) is used for determining low energy reporting in people aged 10 years or over. For more information on the use of this ratio see Under-reporting of energy intakes, the NNPAS 2023 methodology and the NATSINPAS 2023 methodology.

The formulae used to estimate BMR for different population groups are given below (Mifflin-St Jeor 1990). This is a change to the study since 2011–13, which used the Schofield equation method (Schofield 1985), and excluded measured height (cm). This change should be considered when interpreting BMR results over time. A comparison of mean BMR using each method is shown below by age, sex and body mass index.

This is a common issue in dietary recall studies and can include actual changes in foods eaten because people know they will be participating in the survey, or misrepresentation (deliberate, unconscious or accidental), e.g. to make their diets appear healthier or be quicker to report. 

It is important to distinguish between respondent error and genuine behavioural influences. Real-world factors that may affect energy intake include:

• household food insecurity, which can limit access to food and certain food types.
• individual dieting behaviours, such as fasting or dieting.
• seasonal and daily variation in eating habits.
• changes in food composition over time, such as increased consumption of sugar- and fat-reduced products.

A common method for identifying under-reporting of energy intakes is to compare each person's reported energy intake (EI) with their estimated basal metabolic rate (BMR). The ratio of energy to basal metabolic rate (EI:BMR) provides an indication of whether the reported intake is sufficient to live a normal lifestyle (i.e. not bed-bound). There was no adjustment for activity levels or health status.  

The Goldberg cut-off method (Goldberg et al. 1991) is widely used to classify individuals as either:

• Low Energy Reporters (LER) – those with an EI:BMR ratio less than 0.9, meaning their reported intake is less than 90% of their estimated daily resting energy requirements.
• Adequate Energy Reporters (AER) – those with an EI:BMR ratio of 0.9 or higher.

An EI:BMR of less than 0.9 may indicate under-reporting due to social desirability bias, but it can also reflect situational factors such as intentional dieting, fasting, and natural day-to-day variation in food consumption. Estimates of low-energy reporters by selected characteristics are provided in the Under-reporting section of each survey’s methodology.

Energy and some nutrients may be derived from several nutrient components. Within tables, both totals (or equivalents) and nutrient components are presented; however, not all components may be reported.  Totals may not be summed from their components if not all components are reported separately, or the data is averaged. For example, in Table 1 ‘Daily Energy and nutrients from food and beverages, by age and sex’, energy from macronutrients components will not sum to total energy, and the components of fat, polyunsaturated fat, carbohydrate and total sugars do not sum to their respective total. This is due to:

• Total energy: This will be greater than the sum of the macronutrient components (protein, fat, carbohydrate, dietary fibre and alcohol) because total energy accounts for the small amount of energy from organic acids that are not considered carbohydrates.
• Total fat: This includes other forms of fat such as non-fatty acid components of triglycerides, phospholipids, sterols and waxes.
• Total carbohydrate: This includes sugar alcohols, organic acids and other available carbohydrates in addition to sugars and starch.
• Polyunsaturated fat: This includes polyunsaturated fatty acids not included in the values for linoleic acid, alpha- linolenic acid, and total long chain omega 3 fatty acids.

Absolute (total) micronutrient intakes are mainly influenced by the amount of food and drink consumed, and hence energy intake. It is also useful to consider energy-adjusted micronutrient intakes, which are expressed as a nutrient amount per 1,000 kJ of energy. This helps control for factors like age, sex, bodyweight and physical activity that influence energy requirements, and focuses instead on dietary composition.

Some respondents may not have consumed any food or beverages during the recall period, or that the foods and beverages they did consume were calculated to contain zero kilojoules of energy. Respondents with an energy intake of zero kilojoules on day the prior to interview (Day 1) were retained in totals for energy-adjusted intakes. For these respondents, energy-adjusted intakes for all micronutrients were set to 0.

For more information about how each of the nutrients available in AUSNUT nutrient profiles for foods and dietary supplements are derived (and their respective components), visit the FSANZ website.

Assessment of dietary nutrient intakes derived from food recall data are limited by measurement error, and these should be considered. These include:

• Recall bias: Respondents may forget or misreport what they ate, leading to under- or over- estimation.
• Standard measures: Portion weights assigned based on standard food sizes (e.g. medium apple) are calculated based on food analysis and may not reflect the actual size of the foods consumed by the respondents.
• Nutrient profiles: Nutrient profiles are based on food analysis of sampled products, or derivation from the nutrient content of ingredients, and may not reflect the food composition of the foods consumed by the respondents.

During development of the dietary recall, and AUSNUT food measures list, quality assurance was undertaken to ensure gram weights were realistic and reflect the current food supply. Further research has been published on the Intake24 dietary recall tool and the error associated with estimating portion weights compared to other dietary recall tools (Whitton et al. 2024) and assessed against concurrent measurement of total energy expenditure (TEE) using doubly labelled water (Foster et al. 2016). Information about how standard measures and nutrient profiles were developed is available on the FSANZ website.

Iodine and sodium intakes

Iodine and sodium intakes may be impacted by the methods used to collect data on added salt in the study. See Discretionary salt for more information.

Fat intakes

Fat intakes may be impacted by the methods used to derive the fat nutrient profiles for home prepared meals. See Oils and fats for more information.

Pure alcohol

Alcohol reported as a nutrient refers to pure alcohol or ethanol. Estimates of alcohol intake are calculated for alcoholic beverages and foods that contain small amounts due to ingredients. Examples include:

• beer, wine, cider
• cakes
• dressings and condiments (e.g. soy sauce)
• stir-fries
• liqueur filled chocolate.

The primary contributor to pure alcohol estimates comes from alcoholic beverages. See Alcoholic beverages for information about collection of alcoholic beverages consumption.

Total nutrient intakes for a population group may be compared to the relevant Nutrient Reference Values (NRVs) (Eat for Health 2006). It is preferable to use usual nutrient intakes to assess the nutritional status of a population, where possible. See Single day versus usual intakes below.

Nutrient reference values (NRVs) are set by the National Health and Medical Research Council (NHMRC) for different age and sex groups or life stages. The NHMRC is undertaking a rolling review of NRVs, where the new age groups will be used in the future. Updates to the 2006 NRVs for fluoride and sodium were published in 2016 and 2017. See Changes in NHMRC reference age groups for more information.

NRVs such as estimated average requirements (EARs) and upper levels of intake (ULs) are intended to be used at a population level for assessment of the nutrition status of the population of interest (NHMRC 2006). The Suggested Dietary Targets (SDTs) for dietary fibre (38 g/day adult males, 28 g/day adult females) and the revised SDT for sodium (2000 mg/day, adults only) may be referred to in the commentary to put these nutrient intakes in context. Comparison of the NRV to an individual's single day intake does not confirm a specific diagnosis (e.g. a nutrient deficiency) without consultation with a health professional.

Comparison of nutrient intakes estimated from 24-hour recall records with NRVs are not necessarily the best measure of the proportion of the population with nutrient deficiency or excess for some nutrients, particularly for vitamin D, iodine and sodium. Biomedical measures were also taken in the National Health Measures Study (NHMS) 2022–24 to reliably collect information on key vitamins and minerals from a sample of NNPAS and NATSINPAS respondents. Data on iron, calcium, vitamin D, folate and vitamin B12 were collected for participating respondents aged 12 years and over, and on sodium, potassium and iodine for participating respondents aged 5 years and over. See Biomedical collections for more information.

For NNPAS 2023, nutrient and food consumption for the first (Day 1) and second 24-hour recall (Day 2) can be used to estimate usual (habitual) intakes for selected population groups.

Food consumption derived from a single 24-hour recall may not represent the usual consumption patterns of a person because there is often variation in foods consumed on a day-to-day basis. The second 24-hour recall is used to estimate and reduce within-person variation to estimate a usual distribution for the population; this can be either for food consumption amounts by AUSNUT food group or serves of ADG foods and/or nutrient intakes. It is preferable to use usual food intakes or usual serves to assess dietary patterns and usual nutrient intakes to assess the nutritional status of a population, where possible.

The NATSINPAS 2023 only collected a single day of recall.

Data presented in the Foods and Nutrients release for NNPAS and NATSINPAS are calculated based on the Day 1 recall, that is the 24 hours (from midnight to midnight) prior to interview.

Usual intake of nutrients for NNPAS will be released at a later stage.

Data items and related output categories for this topic are available from the NNPAS Data Item List and the NATSINPAS Data Item List.

Eat for Health (2006), Nutrient Reference Values: Macronutrient balance, National Health and Medical Research Council (NHMRC), accessed 25/07/2025.

Foster E, Lee C, Imamura F, Hollidge SE, Westgate KL, Venables MC, Poliakov I, Rowland MK, Osadchiy T, Bradley JC, Simpson EL, Adamson AJ, Olivier P, Wareham N, Forouhi NG, Brage S (2019), Validity and reliability of an online self-report 24-h dietary recall method (Intake24): a doubly labelled water study and repeated-measures analysis, Journal of Nutritional Science, 30(8):e29, accessed 25/07/2025.

Goldberg, GR, Black, AE, Jebb, SA, Cole, TJ, Murgatroyd, PR, Coward, WA, & Prentice, AM (1991), Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording, European Journal of Clinical Nutrition, 45(12):569-581, accessed 25/07/2025.

Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO (1990), A new predictive equation for resting energy expenditure in healthy individuals, The American Journal of Clinical Nutrition, 51(2):241-247, accessed 25/07/2025.

Schofield WN (1985), Predicting basal metabolic rate, new standards and review of previous work, Human Nutrition: Clinical Nutrition, 39(1):5-41, accessed 25/07/2025.

Whitton C, Collins CE, Mullan BA, Rollo ME, Dhaliwal SS, Norman R, Boushey CL, Delp EJ, Zhu F, McCaffrey TA, Kirkpatrick SI, Pollard CM, Healy JD, Hassan A, Garg S, Atyeo P, Mukhtar SA, Kerr DA (2024), Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study, The American Journal of Clinical Nutrition, 120(1):196-210, accessed 25/07/2025.