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      Real-world nudging, pricing, and mobile physical activity coaching was insufficient to improve lifestyle behaviours and cardiometabolic health: the Supreme Nudge parallel cluster-randomised controlled supermarket trial

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          Abstract

          Background

          Context-specific interventions may contribute to sustained behaviour change and improved health outcomes. We evaluated the real-world effects of supermarket nudging and pricing strategies and mobile physical activity coaching on diet quality, food-purchasing behaviour, walking behaviour, and cardiometabolic risk markers.

          Methods

          This parallel cluster-randomised controlled trial included supermarkets in socially disadvantaged neighbourhoods across the Netherlands with regular shoppers aged 30–80 years. Supermarkets were randomised to receive co-created nudging and pricing strategies promoting healthier purchasing ( N = 6) or not ( N = 6). Nudges targeted 9% of supermarket products and pricing strategies 3%. Subsequently, participants were individually randomised to a control (step counter app) or intervention arm (step counter and mobile coaching app) to promote walking. The primary outcome was the average change in diet quality (low (0) to high (150)) over all follow-up time points measured with a validated 40-item food frequency questionnaire at baseline and 3, 6, and 12 months. Secondary outcomes included healthier food purchasing (loyalty card-derived), daily step count (step counter app), cardiometabolic risk markers (lipid profile and HbA1c via finger prick, and waist circumference via measuring tape), and supermarket customer satisfaction (questionnaire-based: very unsatisfied (1) to very satisfied (7)), evaluated using linear mixed-models. Healthy supermarket sales (an exploratory outcome) were analysed via controlled interrupted time series analyses.

          Results

          Of 361 participants (162 intervention, 199 control), 73% were female, the average age was 58 (SD 11) years, and 42% were highly educated. Compared to the control arm, the intervention arm showed no statistically significant average changes over time in diet quality ( β − 1.1 (95% CI − 3.8 to 1.7)), percentage healthy purchasing ( β 0.7 ( − 2.7 to 4.0)), step count ( β − 124.0 (− 723.1 to 475.1), or any of the cardiometabolic risk markers. Participants in the intervention arm scored 0.3 points (0.1 to 0.5) higher on customer satisfaction on average over time. Supermarket-level sales were unaffected ( β − 0.0 (− 0.0 to 0.0)).

          Conclusions

          Co-created nudging and pricing strategies that predominantly targeted healthy products via nudges were unable to increase healthier food purchases and intake nor improve cardiometabolic health. The mobile coaching intervention did not affect step count. Governmental policy measures are needed to ensure more impactful supermarket modifications that promote healthier purchases.

          Trial registration

          Dutch Trial Register ID NL7064, 30 May 2018, https://www.onderzoekmetmensen.nl/en/trial/20990

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12916-024-03268-4.

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          Most cited references78

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          Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019

          Cardiovascular diseases (CVDs), principally ischemic heart disease (IHD) and stroke, are the leading cause of global mortality and a major contributor to disability. This paper reviews the magnitude of total CVD burden, including 13 underlying causes of cardiovascular death and 9 related risk factors, using estimates from the Global Burden of Disease (GBD) Study 2019. GBD, an ongoing multinational collaboration to provide comparable and consistent estimates of population health over time, used all available population-level data sources on incidence, prevalence, case fatality, mortality, and health risks to produce estimates for 204 countries and territories from 1990 to 2019. Prevalent cases of total CVD nearly doubled from 271 million (95% uncertainty interval [UI]: 257 to 285 million) in 1990 to 523 million (95% UI: 497 to 550 million) in 2019, and the number of CVD deaths steadily increased from 12.1 million (95% UI:11.4 to 12.6 million) in 1990, reaching 18.6 million (95% UI: 17.1 to 19.7 million) in 2019. The global trends for disability-adjusted life years (DALYs) and years of life lost also increased significantly, and years lived with disability doubled from 17.7 million (95% UI: 12.9 to 22.5 million) to 34.4 million (95% UI:24.9 to 43.6 million) over that period. The total number of DALYs due to IHD has risen steadily since 1990, reaching 182 million (95% UI: 170 to 194 million) DALYs, 9.14 million (95% UI: 8.40 to 9.74 million) deaths in the year 2019, and 197 million (95% UI: 178 to 220 million) prevalent cases of IHD in 2019. The total number of DALYs due to stroke has risen steadily since 1990, reaching 143 million (95% UI: 133 to 153 million) DALYs, 6.55 million (95% UI: 6.00 to 7.02 million) deaths in the year 2019, and 101 million (95% UI: 93.2 to 111 million) prevalent cases of stroke in 2019. Cardiovascular diseases remain the leading cause of disease burden in the world. CVD burden continues its decades-long rise for almost all countries outside high-income countries, and alarmingly, the age-standardized rate of CVD has begun to rise in some locations where it was previously declining in high-income countries. There is an urgent need to focus on implementing existing cost-effective policies and interventions if the world is to meet the targets for Sustainable Development Goal 3 and achieve a 30% reduction in premature mortality due to noncommunicable diseases.
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            Interrupted time series regression for the evaluation of public health interventions: a tutorial

            Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
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              Consort 2010 statement: extension to cluster randomised trials

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                Author and article information

                Contributors
                j.stuber@amsterdamumc.nl
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                1741-7015
                2 February 2024
                2 February 2024
                2024
                : 22
                : 52
                Affiliations
                [1 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Epidemiology and Data Science, , Amsterdam UMC Location Vrije Universiteit Amsterdam, ; De Boelelaan 1117, Amsterdam, the Netherlands
                [2 ]GRID grid.16872.3a, ISNI 0000 0004 0435 165X, Amsterdam Public Health, ; Amsterdam, the Netherlands
                [3 ]Upstream Team, www.upstreamteam.nl, Amsterdam UMC, De Boelelaan 1117, Amsterdam, the Netherlands
                [4 ]Department of Communication Science, University of Antwerp, ( https://ror.org/008x57b05) St-Jacobstraat 2, 2000 Antwerp, Belgium
                [5 ]Department of Social, Health and Organizational Psychology, Utrecht University, ( https://ror.org/04pp8hn57) Utrecht, the Netherlands
                [6 ]GRID grid.5335.0, ISNI 0000000121885934, Centre for Diet and Activity Research, MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, , University of Cambridge, ; Cambridge, CB2 0QQ UK
                [7 ]GRID grid.5477.1, ISNI 0000000120346234, Julius Center for Health Sciences and Primary Care, , University Medical Center Utrecht, Utrecht University, ; Universiteitsweg 100, Utrecht, the Netherlands
                [8 ]Amsterdam School of Communication Research, University of Amsterdam, ( https://ror.org/04dkp9463) Nieuwe Achtergracht 166, Amsterdam, the Netherlands
                [9 ]GRID grid.491176.c, ISNI 0000 0004 0395 4926, Netherlands Nutrition Centre (Voedingscentrum), ; Bezuidenhoutseweg 105, The Hague, The Netherlands
                [10 ]Public and Occupational Health, Amsterdam UMC Location University of Amsterdam, ( https://ror.org/04dkp9463) Meibergdreef 9, Amsterdam, the Netherlands
                Author information
                http://orcid.org/0000-0001-7825-018X
                http://orcid.org/0000-0002-2783-721X
                http://orcid.org/0000-0001-9759-3938
                http://orcid.org/0000-0002-6634-5680
                http://orcid.org/0000-0003-0303-4540
                http://orcid.org/0000-0001-7932-2042
                http://orcid.org/0000-0002-8237-9506
                http://orcid.org/0000-0002-4605-435X
                http://orcid.org/0000-0002-6913-4897
                http://orcid.org/0000-0002-0164-8306
                http://orcid.org/0000-0002-2218-1119
                http://orcid.org/0000-0003-0956-178X
                http://orcid.org/0000-0002-8551-6748
                http://orcid.org/0000-0002-4181-0937
                Article
                3268
                10.1186/s12916-024-03268-4
                10835818
                38303069
                8a64f35b-5fd8-4978-b735-349a5912349f
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 26 September 2023
                : 22 January 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002996, Hartstichting;
                Award ID: CVON2016-04
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001826, ZonMw;
                Award ID: 531003001
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Medicine
                food environment,grocery store,choice architecture,prevention,obesity,cardiovascular disease
                Medicine
                food environment, grocery store, choice architecture, prevention, obesity, cardiovascular disease

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