Natasha Clarke

AimsImproved alcohol labels are a population-level intervention with the potential to reduce selection and consumption of alcohol. The primary aim of this study was to assess the acceptability of implementing (i) alcohol calorie labelling, (ii) alcohol health warning labels (HWLs), and (iii) both types of labels combined, each presented with or without images. The specific labels used were based on those being proposed for national implementation in Ireland.MethodsParticipants (n=994) were randomly assigned to one of six groups in an online experiment with a between subject 3 (Label Type: HWL only; calorie labelling only; combined label [HWLs and calorie labelling]) x 2 (Message Type: description; description plus example image) factorial design. The primary outcome was acceptability of implementing the labelling policy. Secondary outcomes were perceived effectiveness and potential harms. Free-text comments were analysed using content analysis.ResultsThere was a significant main effect of Label Type (F(2, 990)=4.27, p=0.014, ηₚ²=0.01), with acceptability lower for HWLs (p=0.01, d=0.23). There was no main effect of Message Type or interaction. All types of labels were perceived as relatively acceptable. There were no overall differences in perceived effectiveness or harms of the labels. ConclusionsHWLs and calorie information were both perceived as relatively acceptable, although HWLs were less accepted. The way the labelling policy was presented did not affect acceptability. Although further evaluation with other labelling interventions is warranted, this suggests giving the public more detailed and visual information about a labelling policy does not decrease acceptability of the policy. Trial registration: https://osf.io/h6bzx

The main reason people support government policies is that they believe the policy will be effective. But effective at achieving what outcome? And what happens when people disagree about whether that outcome is good or bad? We address these questions by testing whether Expectancy-Value Theory can be applied to policy support in seven studies (total N = 14,102) for three policies (red meat tax, alcohol calorie labels, points-based immigration system). In a pilot study we gather the public’s beliefs about the consequences of implementing the three policies. In Studies 1a-c, we conducted three pre-registered cross-sectional surveys and found that attitudes towards policy outcomes moderates the relationship between policy outcome beliefs and policy support. In Studies 2a-c, we conducted three pre-registered randomised pre-post-controlled experiments and replicate the main conclusions from Studies 1a-c. These studies find clear evidence that Expectancy-Value Theory explains public support for policies in multiple policy domains.

Background: Overconsumption of food and consumption of any amount of alcohol increases the risk of non-communicable diseases. Calorie (energy) labelling is advocated as a means to reduce energy intake from food and alcoholic drinks. However, there is continued uncertainty about these potential impacts, with a 2018 Cochrane review identifying only a small body of low-certainty evidence. This review updates and extends the 2018 Cochrane review to provide a timely reassessment of evidence for the effects of calorie labelling on people's selection and consumption of food or alcoholic drinks.Objectives: – To estimate the effect of calorie labelling for food (including non-alcoholic drinks) and alcoholic drinks on selection (with or without purchasing) and consumption.– To assess possible modifiers – label type, setting, and socioeconomic status – of the effect of calorie labelling on selection (with or without purchasing) and consumption of food and alcohol.Search methods: We searched CENTRAL, MEDLINE, Embase, PsycINFO, five other published or grey literature databases, trial registries, and key websites, followed by backwards and forwards citation searches. Using a semi-automated workflow, we searched for and selected records and corresponding reports of eligible studies, with these searches current to 2 August 2021. Updated searches were conducted in September 2023 but their results are not fully integrated into this version of the review.Selection criteria: Eligible studies were randomised controlled trials (RCTs) or quasi-RCTs with between-subjects (parallel group) or within-subjects (cross-over) designs, interrupted time series studies, or controlled before-after studies comparing calorie labelling with no calorie labelling, applied to food (including non-alcoholic drinks) or alcoholic drinks. Eligible studies also needed to objectively measure participants' selection (with or without purchasing) or consumption, in real-world, naturalistic laboratory, or laboratory settings.Data collection and analysis: Two review authors independently selected studies for inclusion and extracted study data. We applied the Cochrane RoB 2 tool and ROBINS-I to assess risk of bias in included studies. Where possible, we used (random-effects) meta-analyses to estimate summary effect sizes as standardised mean differences (SMDs) with 95% confidence intervals (CIs), and subgroup analyses to investigate potential effect modifiers, including study, intervention, and participant characteristics. We synthesised data from other studies in a narrative summary. We rated the certainty of evidence using GRADE.Main results: We included 25 studies (23 food, 2 alcohol and food), comprising 18 RCTs, one quasi-RCT, two interrupted time series studies, and four controlled before-after studies. Most studies were conducted in real-world field settings (16/25, with 13 of these in restaurants or cafeterias and three in supermarkets); six studies were conducted in naturalistic laboratories that attempted to mimic a real-world setting; and three studies were conducted in laboratory settings. Most studies assessed the impact 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 was simple calorie labelling (20/25), with other studies assessing calorie labelling with information about at least one other nutrient, or calories with physical activity calorie equivalent (PACE) labelling (or both). Twenty-four studies were conducted in high-income countries, with 15 in the USA, six in the UK, one in Ireland, one in France, and one in Canada. Most studies (18/25) were conducted in high socioeconomic status populations, while six studies included both low and high socioeconomic groups, and one study included only participants from low socioeconomic groups. Twenty-four studies included a measure of selection of food (with or without purchasing), most of which measured selection with purchasing (17/24), and eight studies 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, 9850 participants; high-certainty evidence), with near-identical effects when including only studies at low risk of bias, and when including only studies of selection with purchasing. There may be a larger reduction in consumption (SMD −0.19, 95% CI −0.33 to −0.05; 8 randomised studies, 10 comparisons, 2134 participants; low-certainty evidence). These effect 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 effect observed in the six non-randomised studies 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). Their results were inconclusive, with inconsistent effects and wide 95% CIs encompassing both harm and benefit, and the evidence was of very low certainty. Authors' conclusions: Current evidence suggests that calorie labelling of food (including non-alcoholic drinks) on menus, products, and packaging leads to small reductions in energy selected and purchased, with potentially meaningful impacts on population health when applied at scale. The evidence assessing the impact of calorie labelling of food on consumption suggests a similar effect to that observed for selection and purchasing, although there is less evidence and it is of lower certainty. There is insufficient evidence to estimate the effect of calorie...

Aims: The study aimed to estimate the impact of introducing a draught alcohol‐free beer, thereby increasing the relative availability of these products, on alcohol sales and monetary takings in bars and pubs in England.Design: Randomised crossover field trial.Setting: England.Participants: Fourteen venues that did not previously sell draught alcohol‐free beer.Intervention and comparator: Venues completed two intervention periods and two control periods in a randomised order over 8 weeks. Intervention periods involved replacing one draught alcoholic beer with an alcohol‐free beer. Control periods operated business as usual.Measurements: The primary outcome was mean weekly volume (in litres) of draught alcoholic beer sold. The secondary outcome was mean weekly revenue [in GBP (£)] from all drinks. Analyses adjusted for randomised order, special events, season and busyness.Findings: The adjusted mean difference in weekly sales of draught alcoholic beer was −20 L [95% confidence interval (CI) = −41 to +0.4], equivalent to a 4% reduction (95% CI = 8% reduction to 0.1% increase) in the volume of alcoholic draught beer sold when draught alcohol‐free beer was available. Excluding venues that failed at least one fidelity check resulted in an adjusted mean difference of −29 L per week (95% CI = −53 to −5), equivalent to a 5% reduction (95% CI = 8% reduction to 0.8% reduction). The adjusted mean difference in weekly revenue was +61 GBP per week (95% CI = −328 to +450), equivalent to a 1% increase (95% CI = 5% decrease to 7% increase) when draught alcohol‐free beer was available.Conclusions: Introducing a draught alcohol‐free beer in bars and pubs in England reduced the volume of draught alcoholic beer sold by 4% to 5%, with no evidence of the intervention impacting net revenue.

Aims. The study aimed to estimate the impact of introducing a draught alcohol-free beer, thereby increasing the relative availability of these products, on alcohol sales and monetary takings in bars and pubs in England.Design. Randomised crossover field trial.Participants. Fourteen venues that did not previously sell draught alcohol-free beer.Intervention. Venues completed two intervention periods and two control periods in a randomised order over 8 weeks. Intervention periods involved replacing one draught alcoholic beer with an alcohol-free beer. Control periods operated business as usual.Measurements: The primary outcome was mean weekly volume (in litres) of draught alcoholic beer sold. The secondary outcome was mean weekly revenue (in GBP [£]) from all drinks. Analyses adjusted for randomised order; special events; season; and busyness.Findings. The adjusted mean difference in weekly sales of draught alcoholic beer was -20 litres (95% CI -41 to +0.4), equivalent to a 4% reduction (95% CI 8% reduction to 0.1% increase) in the volume of alcoholic draught beer sold when draught alcohol-free beer was available. Excluding venues that failed at least one fidelity check resulted in an adjusted mean difference of -29 litres per week (95% CI -53 to -5), equivalent to a 5% reduction (95% CI 8% reduction to 0.8% reduction). The adjusted mean difference in weekly revenue was +61 GBP per week (95% CI -328 to +450), equivalent to a 1% increase (95% CI 5% decrease to 7% increase) when draught alcohol-free beer was available.Conclusions. Introducing a draught alcohol-free beer in bars and pubs may reduce the volume of draught alcoholic beer sold without substantially impacting net revenue. While further research would help to refine the precision of these estimates, the reduction in sales of alcoholic beer of 4 to 5% could have a positive impact on public health at a population level.

Aims: To estimate the impact on selection and actual purchasing of (a) health warning labels (text‐only and image‐and‐text) on alcoholic drinks and (b) calorie labels on alcoholic and non‐alcoholic drinks.Design: Parallel‐groups randomised controlled trial.Setting: Drinks were selected in a simulated online supermarket, before being purchased in an actual online supermarket.Participants: Adults in England and Wales who regularly consumed and purchased beer or wine online (n = 651). Six hundred and eight participants completed the study and were included in the primary analysis.Interventions: Participants were randomized to one of six groups in a between‐subjects three [health warning labels (HWLs) (i): image‐and‐text HWL; (ii) text‐only HWL; (iii) no HWL] × 2 (calorie labels: present versus absent) factorial design (n per group 103–113).Measurements: The primary outcome measure was the number of alcohol units selected (with intention to purchase); secondary outcomes included alcohol units purchased and calories selected and purchased. There was no time limit for selection. For purchasing, participants were directed to purchase their drinks immediately (although they were allowed up to 2 weeks to do so).Findings: There was no evidence of main effects for either (a) HWLs or (b) calorie labels on the number of alcohol units selected (HWLs: F_(2,599) = 0.406, P = 0.666; calorie labels: F_(1,599) = 0.002, P = 0.961). There was also no evidence of an interaction between HWLs and calorie labels, and no evidence of an overall difference on any secondary outcomes. In pre‐specified subgroup analyses comparing the ‘calorie label only’ group (n = 101) with the ‘no label’ group (n = 104) there was no evidence that calorie labels reduced the number of calories selected (unadjusted means: 1913 calories versus 2203, P = 0.643). Among the 75% of participants who went on to purchase drinks, those in the ‘calorie label only’ group (n = 74) purchased fewer calories than those in the ‘no label’ group (n = 79) (unadjusted means: 1532 versus 2090, P = 0.028).Conclusions: There was no evidence that health warning labels reduced the number of alcohol units selected or purchased in an online retail context. There was some evidence suggesting that calorie labels on alcoholic and non‐alcoholic drinks may reduce calories purchased from both types of drinks.

Background: Increasing the availability of non-alcoholic options is a promising population-level intervention to reduce alcohol consumption, currently unassessed in naturalistic settings. This study in an online retail context aimed to estimate the impact of increasing the proportion of non-alcoholic (relative to alcoholic) drinks, on selection and purchasing of alcohol. Methods and results: Adults (n = 737) residing in England and Wales who regularly purchased alcohol online were recruited between March and July 2021. Participants were randomly assigned to one of 3 groups: “25% non-alcoholic/75% alcoholic”; “50% non-alcoholic/50% alcoholic”; and “75% non-alcoholic/25% alcoholic,” then selected drinks in a simulated online supermarket, before purchasing them in an actual online supermarket. The primary outcome was the number of alcohol units selected (with intention to purchase); secondary outcomes included actual purchasing. A total of 607 participants (60% female, mean age = 38 years [range: 18 to 76]) completed the study and were included in the primary analysis. In the first part of a hurdle model, a greater proportion of participants in the “75% non-alcoholic” group did not select any alcohol (13.1%) compared to the “25% non-alcoholic” group (3.4%; 95% confidence interval [CI] −2.09, −0.63; p < 0.001). There was no evidence of a difference between the “75% non-alcoholic” and the “50% non-alcoholic” (7.2%) groups (95% CI 0.10, 1.34; p = 0.022) or between the “50% non-alcoholic” and the “25% non-alcoholic” groups (95% CI −1.44, 0.17; p = 0.121). In the second part of a hurdle model in participants (559/607) selecting any drinks containing alcohol, the “75% non-alcoholic” group selected fewer alcohol units compared to the “50% non-alcoholic” (95% CI −0.44, −0.14; p < 0.001) and “25% non-alcoholic” (95% CI −0.54, −0.24; p < 0.001) groups, with no evidence of a difference between the “50% non-alcoholic” and “25% non-alcoholic” groups (95% CI −0.24, 0.05; p = 0.178). Overall, across all participants, 17.46 units (95% CI 15.24, 19.68) were selected in the “75% non-alcoholic” group; 25.51 units (95% CI 22.60, 28.43) in the “50% non-alcoholic” group; and 29.40 units (95% CI 26.39, 32.42) in the “25% non-alcoholic” group. This corresponds to 8.1 fewer units (a 32% reduction) in the “75% non-alcoholic” compared to the “50% non-alcoholic” group, and 11.9 fewer alcohol units (41% reduction) compared to the “25% non-alcoholic” group; 3.9 fewer units (13% reduction) were selected in the “50% non-alcoholic” group than in the “25% non-alcoholic” group. For all other outcomes, alcohol selection and purchasing were consistently lowest in the “75% non-alcoholic” group. Study limitations include the setting not being entirely naturalistic due to using a simulated online supermarket as well as an actual online supermarket, and that there was substantial dropout between selection and purchasing. Conclusions: This study provides evidence that substantially increasing the proportion of non-alcoholic drinks—from 25% to 50% or 75%—meaningfully reduces alcohol selection and purchasing. Further studies are warranted to assess whether these effects are realised in a range of real-world settings. Trial registration: ISRCTN: 11004483; OSF: https://osf.io/qfupw.

Objective: Health warning and calorie labels on alcohol have the potential to reduce consumption at population level but remain unevaluated using robust designs with behavioural outcomes. The aim of the current study is to estimate the impact on selection and actual purchasing of (a) health warning labels (text-only and image-and-text) on alcoholic drinks and (b) calorie labels on alcoholic and non-alcoholic drinks. Design: Parallel-groups randomised controlled trial. Setting: Participants selected drinks in a simulated online supermarket, before purchasing them in an actual online supermarket. Participants: Adults in England and Wales who regularly consumed and purchased beer or wine online (n= 644). Interventions: Participants were randomised to one of six groups in a between-subjects 3 ( Health warning labels (HWLs) : i. image-and-text HWL, ii. text-only HWL, iii. no HWL) x 2 ( Calorie labels : present vs absent) factorial design. Main outcome measures: The number of alcohol units selected (with intention to purchase); secondary outcomes included alcohol units purchased and calories selected and purchased. Results: 608 participants completed the study and were included in the primary analysis. There was no evidence of an overall difference for either (a) HWLs, or (b) calorie labels on the number of alcohol units selected [HWLs: F(2,599) = 0.406, p = .666; calorie labels: F(1,599) = 0.002, p = .961]. There was also no evidence of an overall difference on any secondary outcomes, including the number of alcohol units purchased [HWLs: F(2,462) = 1.85, p = .159; calorie labels: F(1,462) = 0.193, p = .661]. In pre-specified subgroup analyses comparing the ‘calorie label only’ group (n = 101) to the ‘no label’ group (n = 104) there was no evidence that calorie labels reduced the number of calories selected [unadjusted means: 1913 calories vs 2203 calories, p = .643]. Amongst the 75% of participants who went on to purchase drinks, those in the ‘calorie label only’ group (n = 74) purchased fewer calories than those in the ‘no label’ group (n = 79) [unadjusted means: 1532 calories vs 2090, p = .028]. Conclusion: There was no evidence that health warning labels reduced the number of alcohol units selected or purchased in an online purchasing setting. There was some evidence suggesting that calorie labels on alcoholic and non-alcoholic drinks may reduce calories purchased. Further evaluation is warranted in suitably powered studies in real world settings. Trial registration: Pre-registered protocol ( https://osf.io/ch2sm/ ) and prospective ISCRTN registration: https://www.isrctn.com/ISRCTN10313219 Funding: This study was funded by Wellcome [Grant number 206853/Z/17/Z].

Background Increasing the availability of non-alcoholic options is a promising population-level intervention to reduce alcohol consumption, currently unassessed in naturalistic settings. This study in an online retail setting aimed to estimate the impact of increasing the proportion of non-alcoholic (relative to alcoholic) drinks, on selection and actual purchasing of alcohol. Methods Adults (n=737) who regularly purchased alcohol online were randomly assigned to one of three groups: Higher Proportion of non-alcoholic drinks available (75%); Same Proportion (50%); Lower Proportion (25%). Participants selected drinks in a simulated online supermarket, before purchasing them in an actual online supermarket. The primary outcome was the number of alcohol units selected (with intention to purchase); secondary outcomes included purchasing. Findings 607 participants completed the study and were included in the primary analysis. The Higher Proportion group selected 10·0 fewer alcohol units than the Lower Proportion group (−32%; 95%CI −42%,−22%) and 7·1 fewer units compared to the Same Proportion group (−25%; 95%CI −36%,−13%), based on model results in those selecting any drinks containing alcohol (559/607). There was no evidence of a difference between the Same Proportion and Lower Proportion groups (2·9 fewer units, −9%; 95%CI −22%,5%). For all other outcomes, alcohol selection and purchasing were consistently lowest in the Higher Proportion group. Interpretation This study provides evidence that substantially increasing the proportion of non-alcoholic drinks − from 25% to 50% or 75% − meaningfully reduces alcohol selection and purchasing. Further studies are warranted to assess whether these effects are realised in a range of real-world settings. Funding Wellcome

Background: There is ongoing clinical and research interest in determining whether providing personalised risk information could motivate risk-reducing health behaviours. We aimed to assess the impact on behaviours and risk factors of feeding back to individuals’ images of their bodies generated via medical imaging technologies in assessing their current disease status or risk.Methods and findings: A systematic review with meta-analysis was conducted using Cochrane methods. MEDLINE, Embase, PsycINFO, CINAHL, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched up to July 28, 2021, with backward and forward citation searches up to July 29, 2021. Eligible studies were randomised controlled trials including adults who underwent medical imaging procedures assessing current health status or risk of disease, for which personal risk may be reduced by modifying behaviour. Trials included an intervention group that received the imaging procedure plus feedback of visualised results and assessed subsequent risk-reducing health behaviour. We examined 12,620 abstracts and included 21 studies, involving 9,248 randomised participants. Studies reported on 10 risk-reducing behaviours, with most data for smoking (8 studies;n= 4,308), medication use (6 studies;n= 4,539), and physical activity (4 studies;n= 1,877). Meta-analysis revealed beneficial effects of feedback of visualised medical imaging results on reduced smoking (risk ratio 1.11, 95% confidence interval [CI] 1.01 to 1.23,p= 0.04), healthier diet (standardised mean difference [SMD] 0.30, 95% CI 0.11 to 0.50,p= 0.003), increased physical activity (SMD 0.11, 95% CI 0.003 to 0.21,p= 0.04), and increased oral hygiene behaviours (SMD 0.35, 95% CI 0.13 to 0.57,p= 0.002). In addition, single studies reported increased skin self-examination and increased foot care. For other behavioural outcomes (medication use, sun protection, tanning booth use, and blood glucose testing) estimates favoured the intervention but were not statistically significant. Regarding secondary risk factor outcomes, there was clear evidence for reduced systolic blood pressure, waist circumference, and improved oral health, and some indication of reduced Framingham risk score. There was no evidence of any adverse effects, including anxiety, depression, or stress, although these were rarely assessed. A key limitation is that there were some concerns about risk of bias for all studies, with evidence for most outcomes being of low certainty. In particular, valid and precise measures of behaviour were rarely used, and there were few instances of preregistered protocols and analysis plans, increasing the likelihood of selective outcome reporting.Conclusions: In this study, we observed that feedback of medical images to individuals has the potential to motivate risk-reducing behaviours and reduce risk factors. Should this promise be corroborated through further adequately powered trials that better mitigate against risk of bias, such interventions could usefully capitalise upon the widespread and growing use of medical imaging technologies in healthcare.