Calorie (energy) labelling for changing selection and consumption of food or alcohol

Abstract

Background Overconsumption of food and consumption of any amount of alcohol increases the risk of non-communicable diseases. Calorie (energy) labelling isadvocated as a means to reduce energy intake from food and alcoholic drinks. There is, however, continued uncertainty about these potential impacts,with a 2018 Cochrane review identifying only a small body of low-quality evidence. This review updates and extends the 2018 Cochrane review toprovide a timely reassessment of evidence for the eff ects of calorie labelling on people’s selection and consumption of food or alcoholic drinks. Objectives

To estimate the eff ect of calorie labelling for food (including non-alcoholic drinks) and alcoholic drinks on selection (with or without purchasing) andconsumption. To assess possible modifi ers – label type, setting and socioeconomic status - of the eff ect of calorie labelling on selection (with or without purchasing)and consumption of food and alcohol. Search methods We searched CENTRAL, MEDLINE, Embase, PsycINFO, fi ve other published or grey literature databases, trial registries and key websites, followed bybackwards and forwards citation searches. A semi-automated workfl ow was used to search for, prioritize and select records and corresponding reportsof eligible studies. These searches are current to 2nd August 2021. Updated searches were conducted in September 2023 but their results are not fullyintegrated into this version of the review. Selection criteria Eligible studies were randomised or quasi-randomised controlled trials (RCTs/Q-RCTs) with between-subjects (parallel group) or within-subjects (cross-over) designs, interrupted time series (ITS) studies, or controlled before-and-after studies, comparing calorie labelling with no calorie labelling, applied tofood - including non-alcoholic drinks - or alcoholic drinks. Eligible studies also needed to objectively measure participants’ selection (with or withoutpurchasing) or consumption, in real-world, naturalistic laboratory, or laboratory settings. Data collection and analysis Two authors independently selected studies for inclusion and extracted study data. We applied the Cochrane RoB 2 tool and ROBINS-I to assess risk ofbias in included studies. Where possible, we used (random-eff ects) meta-analysis to estimate summary eff ect sizes as standardized mean diff erences(SMDs) with 95% confi dence intervals (CIs), and subgroup analyses to investigate potential eff ect modifi ers, including study, intervention, andparticipant characteristics. We synthesized data from other studies in a narrative summary. We rated the certainty of evidence for outcomes usingGRADE. Main results We included 25 studies (23 food, 2 alcohol and food), comprising 18 RCTs, one Q-RCT, two ITS studies, and four CBA studies. Most studies wereconducted in real-world fi eld settings (16/25, with 13 of these in restaurants or cafeterias and three in supermarkets), while six studies were conducted innaturalistic laboratories that attempted to mimic a real-world setting, and three studies were conducted in laboratory settings. Most studies assessed theimpact of calorie labelling on menus or menu boards (18/25); six studies assessed the impact of calorie labelling directly on, or placed adjacent to,products or their packaging; and one study assessed labels on both menus and on product packaging. The most frequently assessed labelling type wassimple calorie labelling (20/25), with other studies assessing calorie labelling with information about at least one other nutrient and/or calories withphysical activity exercise equivalent (PACE) labelling. All but one of the studies (24/25) were conducted in high-income countries, with 15 in the USA, sixin the UK, one in Ireland, one in France, and one in Canada. Most studies (18/25) were conducted in high socioeconomic status populations, while sixstudies included both low and high socioeconomic groups, and one study included only participants from low socioeconomic groups. Nearly all studies(24/25) included a measure of selection of food (with or without purchasing), most of which measured selection with purchasing (17/24), and eightstudies included a measure of consumption of food. Calorie labelling of food led to a small reduction in energy selected: SMD -0.06, 95% CI -0.08 to -0.03; 16 randomised studies; 19 comparisons; n =9850; high-certainty evidence), with near-identical eff ects when including only low risk of bias studies, and when including only studies of selection withpurchasing. There may be a larger reduction in consumption: SMD -0.19, 95% CI -0.33 to -0.05; 8 randomised studies; 10 comparisons; n = 2134; low-certainty evidence. These eff ect sizes suggest that, for an average meal of 600 kcal, adults exposed to calorie labelling would select 11 kcal less(equivalent to a 1.8% reduction), and consume 35 kcal less (equivalent to a 5.9% reduction). The direction of eff ect observed in the six non-randomisedstudies was broadly consistent with that observed in the 16 randomised studies. Only two studies focused on alcoholic drinks, and these studies also included a measure of selection of food (including non-alcoholic drinks). Theirresults were inconclusive, with inconsistent eff ects and wide 95% CIs encompassing both harm and benefi t, and the evidence was thus judged to be ofvery low certainty.