Oral Presentation Joint Annual Scientific Meeting of the Nutrition Society of NZ and the Nutrition Society of Australia

Health Star Rating: development of a trans-Tasman front of pack labelling initiative (300)

Winsome Parnell 1 , Phillippa Hawthorne 2 , Michelle Gibbs 2
  1. Department of Human Nutrition, University of Otago, Dunedin, New Zealand
  2. Food Science and Risk Assessment Directorate, Ministry for Primary Industries, Wellington, New Zealand

Background/Aims: The Health Star Rating (HSR) system was developed by the Australian, state and territory governments in collaboration with industry, public health and consumer groups. The purpose of the system is two-fold: to provide a simple and easy method for consumers to compare the nutritional profile of packaged foods; and provide incentive for reformulation of packaged foods.

Methods: The HSR system uses a star rating scale of 1/2 to 5 stars. The number of stars is determined by an algorithm that considers both the positive and negative aspects of the food product (energy, saturated fat, sugar, sodium, protein, dietary fibre, and the proportion of fruit, vegetable, nut and legume content). 

The algorithm is based on the nutrient profiling scoring criteria developed by Food Standards Australia New Zealand (FSANZ). The algorithm was tested on approximately 3500 packaged foods across 39 sub-categories. It was modified to enable meaningful ‘within food’ category comparisons and to ensure alignment with the Australian Dietary Guidelines. 

Results: Consumer testing was conducted in Australia and New Zealand. HSR positively affected consumers' ability to correctly identify products. New Zealand joined the HSR system following assessment of the system against a set of principles developed by the New Zealand Front-of-Pack Labelling Advisory Group, namely: the need for an interpretive system, meaningful within food categories, readily understood by consumers, and supporting government nutrition policy.

Conclusions: Formative research indicated that the HSR system is able to assist shopper decision making and provide maximum differentiation between foods within categories.

Funding source:  N/A