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

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 -0.20 (95% CI: -0.94, 0.54; P = 0.60). The corresponding difference for HSR compared with NIP was -0.60 (95% CI: -1.35, 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: -1.33 (95% CI: -2.63, -0.04; P = 0.04); HSR compared with NIP: -1.70 (95% CI: -2.97, -0.43; 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.

[1]  H. Eyles,et al.  “Smart” RCTs: Development of a Smartphone App for Fully Automated Nutrition-Labeling Intervention Trials , 2016, JMIR mHealth and uHealth.

[2]  M. Cecchini,et al.  Impact of food labelling systems on food choices and eating behaviours: a systematic review and meta‐analysis of randomized studies , 2016, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[3]  Ashutosh Kumar Singh,et al.  Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015 , 2016, Lancet.

[4]  Ross A. Hammond,et al.  Smart food policies for obesity prevention , 2015, The Lancet.

[5]  B. Neal,et al.  Nutrient profile of 23 596 packaged supermarket foods and non-alcoholic beverages in Australia and New Zealand , 2015, Public Health Nutrition.

[6]  B. Swinburn,et al.  Effects of interpretive front-of-pack nutrition labels on food purchases: protocol for the Starlight randomised controlled trial , 2014, BMC Public Health.

[7]  Ka Hung Ng,et al.  FoodSwitch: A Mobile Phone App to Enable Consumers to Make Healthier Food Choices and Crowdsourcing of National Food Composition Data , 2014, JMIR mHealth and uHealth.

[8]  J. Lee,et al.  Effects of the Guiding Stars Program on purchases of ready-to-eat cereals with different nutritional attributes , 2013 .

[9]  Tim Marsh,et al.  Changing the future of obesity: science, policy, and action , 2011, The Lancet.

[10]  Martin McKee,et al.  UN High-Level Meeting on Non-Communicable Diseases: addressing four questions , 2011, The Lancet.

[11]  B. Swinburn,et al.  Impact of ‘traffic‐light’ nutrition information on online food purchases in Australia , 2011, Australian and New Zealand journal of public health.

[12]  David Hammond,et al.  Nutrition labels on pre-packaged foods: a systematic review , 2011, Public Health Nutrition.

[13]  Johannes Brug,et al.  Front-of-pack nutrition label stimulates healthier product development: a quantitative analysis , 2010, The international journal of behavioral nutrition and physical activity.

[14]  In-Hwan Kim,et al.  Trans fatty acids content and fatty acid profiles in the selected food products from Korea between 2005 and 2008. , 2010, Journal of food science.

[15]  Lisa A Sutherland,et al.  Guiding stars: the effect of a nutrition navigation program on consumer purchases at the supermarket. , 2010, The American journal of clinical nutrition.

[16]  B. Neal,et al.  A systematic survey of the sodium contents of processed foods. , 2010, The American journal of clinical nutrition.

[17]  B. Swinburn,et al.  Impact of front-of-pack 'traffic-light' nutrition labelling on consumer food purchases in the UK. , 2009, Health promotion international.

[18]  Mei-Hua Chen,et al.  Nutrition labels: a survey of use, understanding and preferences among ethnically diverse shoppers in New Zealand , 2009, Public Health Nutrition.

[19]  Boyd Swinburn,et al.  Impact of the Pick the Tick food information programme on the salt content of food in New Zealand. , 2002, Health promotion international.