The Impact of Nutri-score Labels on the Nutritional Quality of Out-of-home Food Selection in a Delivery Platform Setting: An Online Randomised Controlled Trial

Healthiness of selected meal

时间窗: Day 1 of 1: Food orders (item names, energy content, UK Nutrient Profiling Model scores) will be recorded immediately after the baseline questionnaire. The recorded information will be used to calculate weighted NPM scores.

In line with published research into Nutri-score, the investigators will assess the healthiness of meals using UK Nutrient Profiling Model (NPM) scores(16). These scores are calculated in a similar way to Nutri-score, whereby the total value of positive nutrients is subtracted from the total value of negative nutrients. Lower scores represent healthier items. Scores range from -15 (healthiest) to 40 (least healthy). To account for the likely event of multiple items being selected to form a meal, the investigators will calculate a mean NPM score, weighted by the energy content of individual food items. This will be done using the 'weighted.mean'(37) function in R grouped by participant so the weights are specific to individual orders. The following code will be used for this: weighted\_ppt\<-combine %\>% group\_by(ppt) %\>% summarise(score = weighted.mean(nutriscore, meal\_kcal))

Nutri-score label of selected food items

时间窗: Day 1 of 1: Food orders (item names, NS values) will be recorded immediately after the baseline questionnaire when participants select their food orders.

Nutri-score values of each menu item were calculated prior to creating the online ordering platform. To calculate overall scores, the total value of positive nutrients is subtracted from the total value of negative nutrients. Lower scores represent healthier items. Thresholds are used to dictate the Nutri-score value assigned to items, with A (healthiest) to E (least healthy) When participants complete the food choice task, the Nutri-score labels (A-E) of each item selected by participants will be recorded by the online platform.