Nutrient profile of 23 596 packaged supermarket foods and non-alcoholic beverages in Australia and New Zealand

Mhurchú, Cliona Ní; Brown, Ryan M; Jiang, Yannan; Eyles, Helen; Dunford, Elizabeth; Neal, Bruce

doi:10.1017/s1368980015000968

Cited by 40 publications

(38 citation statements)

References 19 publications

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“…Our study found that a higher proportion of products met the NPSC (51 %) compared with a recent study conducted on the entire Australian dairy category (33 %) (24) . The difference is likely due to the fact that the present study includes only core dairy products, while the other included the entire dairy category, with non-core products such as cream being included.…”

Section: Discussioncontrasting

confidence: 75%

Investigating nutrient profiling and Health Star Ratings on core dairy products in Australia

Wellard

Hughes

Watson

2016

Public Health Nutr.

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Objective: To determine whether the ratings from the Australian front-of-pack labelling scheme, Health Star Rating (HSR), and the ability to carry health claims using the Nutrient Profiling Scoring Criterion (NPSC) for core dairy products promote foods consistent with the Australian Dietary Guidelines. Design: The Australian nutrient profiling model used for assessing eligibility for health claims was compared with the nutrient profiling model underpinning the HSR system to determine their agreement when assessing dairy products. Agreement between the extent to which products met nutrient profiling criteria and scored three stars or over using the HSR calculator was determined using Cohen's kappa tests. Setting: The four largest supermarket chains in Sydney, Australia. Subjects: All available products in the milk, hard cheese, soft cheese and yoghurt categories (n 1363) were surveyed in March-May 2014. Nutrition composition and ingredients lists were recorded for each product. Results: There was 'good' agreement between NPSC and HSR overall (κ = 0·78; 95 % CI 0·75, 0·81; P < 0·001), for hard cheeses (κ = 0·72; 95 % CI 0·65, 0·79; P < 0·001) and yoghurt (κ = 0·79; 95 % CI 0·73, 0·86; P < 0·001). There was 'fair' agreement for milk (κ = 0·33; 95 % CI 0·20, 0·45; P < 0·001) and 'very good' agreement for soft cheese (κ = 0·84; 95 % CI 0·75,

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Section: Discussioncontrasting

confidence: 75%

Investigating nutrient profiling and Health Star Ratings on core dairy products in Australia

Wellard

Hughes

Watson

2016

Public Health Nutr.

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“…With the assumption of an SD of 9.9, which was the actual variance of NPSC scores in a similar Australian database of branded food compositions (19), a sample size of w1500 individuals was estimated to provide 80% power at a 5% level of significance (2 sided) to detect a 2-U difference in the mean NPSC score between either of the intervention arms and the control group with adjustment for multiple comparisons. A subsequent analysis of NPSC scores of New Zealand packaged foods that was published in 2015 (n = 8377 foods) reported a mean 6 SD NPSC score of 7.8 6 9.7 and a range from 216 to 45 (20). A 2-U difference in the NPSC score was chosen because it was considered to be a meaningful change at a population level and was equivalent to the following approximate differences in nutrient contents per 100 g food: 78 kJ energy, 0.95 g saturated fat, 1.5 g total sugars, and 66 mg Na (20).…”

Section: Sample Size and Data Analysismentioning

confidence: 99%

“…A subsequent analysis of NPSC scores of New Zealand packaged foods that was published in 2015 (n = 8377 foods) reported a mean 6 SD NPSC score of 7.8 6 9.7 and a range from 216 to 45 (20). A 2-U difference in the NPSC score was chosen because it was considered to be a meaningful change at a population level and was equivalent to the following approximate differences in nutrient contents per 100 g food: 78 kJ energy, 0.95 g saturated fat, 1.5 g total sugars, and 66 mg Na (20).…”

Section: Sample Size and Data Analysismentioning

confidence: 99%

Effects of interpretive nutrition labels on consumer food purchases: the Starlight randomized controlled trial ,

Mhurchú

Volkova

Jiang

et al. 2017

The American Journal of Clinical Nutrition

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Background: Nutrition labeling is a prominent policy to promote healthy eating. Objective: We aimed to evaluate the effects of 2 interpretive nutrition labels compared with a noninterpretive label on consumer food purchases. Design: In this parallel-group randomized controlled trial, we enrolled household shoppers across New Zealand who owned smartphones and were aged $18 y. Eligible participants were randomly assigned (1:1:1) to receive either traffic light labels (TLLs), Health Star Rating labels (HSRs), or a control [nutrition information panel (NIP)]. Smartphone technology allowed participants to scan barcodes of packaged foods and to receive allocated labels on their smartphone screens. The primary outcome was the mean healthiness of all packaged food purchases over the 4-wk intervention period, which was measured by using the Food Standards Australia New Zealand Nutrient Profiling Scoring Criterion (NPSC). Results: Between October 2014 and November 2015, 1357 eligible shoppers were randomly assigned to TLL (n = 459), HSR (n = 443), or NIP (n = 455) labels. Overall difference in the mean transformed NPSC score for the TLL group compared with the NIP group was 20.20 (95% CI: 20.94, 0.54; P = 0.60). The corresponding difference for HSR compared with NIP was 20.60 (95% CI: 21.35, 0.15; P = 0.12). In an exploratory per-protocol analysis of participants who used the labeling intervention more often than average (n = 423, 31%), those who were assigned to TLL and HSR had significantly better NPSC scores [TLL compared with NIP: 21.33 (95% CI: 22.63, 20.04; P = 0.04); HSR compared with NIP: 21.70 (95% CI: 22.97, 20.43; P = 0.01)]. Shoppers who were randomly assigned to HSR and TLL also found the labels significantly more useful and easy to understand than the NIP (all P values ,0.001). Conclusions: At the relatively low level of use observed in this trial, interpretive nutrition labels had no significant effect on food purchases. However, shoppers who used interpretive labels found them to be significantly more useful and easy to understand, and compared with frequent NIP users, frequent TLL and HSR users had significantly healthier food purchases. This trial was registered at the Australian New Zealand Clinical Trials Registry (https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id= 366446&isReview=true) as ACTRN12614000644662.Am J Clin Nutr 2017;105:695-704.

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“…Classification of packaged foods as healthy and unhealthy was reported . Nutrient profiling models utilized included the Food Standards Australia New Zealand (FSANZ) Nutrient Profiling Scoring criterion, which is used to determine whether a food is suitable to make a health claim ; the New South Wales School Canteen criteria, criteria developed for an Australian food company, and the United Kingdom Traffic Light criteria ; the Health Star Rating front‐of‐pack labelling device scores ; and the Australian Dietary Guidelines . Changes in energy, total fat and protein content of yogurts and dairy desserts were assessed over time .…”

Section: Resultsmentioning

confidence: 99%

What is known about consumer nutrition environments in Australia? A scoping review of the literature

Pulker

Thornton

Trapp

2018

Obesity Science & Practice

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SummaryObjectiveFood environments can influence food selection and hold the potential to reduce obesity, non‐communicable diseases and their inequalities. ‘Consumer nutrition environments’ describe what consumers encounter within a food retail outlet, including products, price, promotion and placement. This study aimed to summarize the attributes that have been examined in existing peer‐reviewed studies of Australian consumer nutrition environments, identify knowledge gaps and provide recommendations for future research.MethodsA systematic search of peer‐reviewed literature was conducted. Sixty‐six studies that assessed an aspect of within‐store consumer nutrition environments were included.ResultsMost studies were published from 2011 onwards and were conducted in capital cities and in supermarkets. Studies assessed the domains of product (40/66), price (26/66), promotion (16/66) and placement (6/66). The most common research themes identified were assessment of the impact of area socioeconomic status (13/66), remoteness (9/66) and food outlet type (7/66) on healthy food prices; change in price of healthy foods (6/66); variety of healthy foods (5/66); and prevalence of unhealthy child‐orientated products (5/66).ConclusionsThis scoping review identified a large number of knowledge gaps. Recommended priorities for researchers are as follows: (1) develop consistent observational methodology, (2) consider consumer nutrition environments in rural and remote communities, (3) develop an understanding of food service outlets, (4) build on existing evidence in all four domains of product, price, placement and promotion and (5) determine effective policy and store‐based interventions to increase healthy food selection.

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Nutrient profile of 23 596 packaged supermarket foods and non-alcoholic beverages in Australia and New Zealand

Cited by 40 publications

References 19 publications

Investigating nutrient profiling and Health Star Ratings on core dairy products in Australia

Investigating nutrient profiling and Health Star Ratings on core dairy products in Australia

Effects of interpretive nutrition labels on consumer food purchases: the Starlight randomized controlled trial ,

What is known about consumer nutrition environments in Australia? A scoping review of the literature

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