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      Reducing cardiometabolic risk in adults with a low socioeconomic position: protocol of the Supreme Nudge parallel cluster-randomised controlled supermarket trial

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          Abstract

          Background

          Unhealthy lifestyle behaviours such as unhealthy dietary intake and insufficient physical activity (PA) tend to cluster in adults with a low socioeconomic position (SEP), putting them at high cardiometabolic disease risk. Educational approaches aiming to improve lifestyle behaviours show limited effect in this population. Using environmental and context-specific interventions may create opportunities for sustainable behaviour change. In this study protocol, we describe the design of a real-life supermarket trial combining nudging, pricing and a mobile PA app with the aim to improve lifestyle behaviours and lower cardiometabolic disease risk in adults with a low SEP.

          Methods

          The Supreme Nudge trial includes nudging and pricing strategies cluster-randomised on the supermarket level, with: i) control group receiving no intervention; ii) group 1 receiving healthy food nudges (e.g., product placement or promotion); iii) group 2 receiving nudges and pricing strategies (taxing of unhealthy foods and subsidizing healthy foods). In collaboration with a Dutch supermarket chain we will select nine stores located in low SEP neighbourhoods, with the nearest competitor store at > 1 km distance and managed by a committed store manager. Across the clusters, a personalized mobile coaching app targeting walking behaviour will be randomised at the individual level, with: i) control group; ii) a group receiving the mobile PA app. All participants (target n = 1485) should be Dutch-speaking, aged 45–75 years with a low SEP and purchase more than half of their household grocery shopping at the selected supermarkets. Participants will be recruited via advertisements and mail-invitations followed by community-outreach methods. Primary outcomes are changes in systolic blood pressure, LDL-cholesterol, HbA1c and dietary intake after 12 months follow-up. Secondary outcomes are changes in diastolic blood pressure, blood lipid markers, waist circumference, steps per day, and behavioural factors including healthy food purchasing, food decision style, social cognitive factors related to nudges and to walking behaviours and customer satisfaction after 12 months follow-up. The trial will be reflexively monitored to support current and future implementation.

          Discussion

          The findings can guide future research and public health policies on reducing lifestyle-related health inequalities, and contribute to a supermarket-based health promotion intervention implementation roadmap.

          Trial registration

          Dutch Trial Register ID NL7064, 30th of May, 2018

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          Most cited references62

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          The development of scales to measure social support for diet and exercise behaviors.

          The purpose of this study was to develop measures of perceived social support specific to health-related eating and exercise behaviors. In Study I, specific supportive and nonsupportive behaviors were identified through interviews with 40 individuals making health-behavior changes. In Study II, items derived from the interviews were administered to 171 subjects. Support from family and friends was assessed separately for both diet and exercise habits. Meaningful factors were identified for each of the four scales, and some factors were similar for family and friend scales. Both test-retest and internal consistency reliabilities were acceptable, and six factors can be used as subscales. Social support scales were correlated with respective self-reported dietary and exercise habits, providing evidence of concurrent criterion-related validity. A measure of general social support was not related to the specific social support scales or to reported health habits. These scales are among the first measures of social support behaviors specific to dietary- and exercise-habit change.
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            Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach

            Background Mobile phone use and the adoption of healthy lifestyle software apps (“health apps”) are rapidly proliferating. There is limited information on the users of health apps in terms of their social demographic and health characteristics, intentions to change, and actual health behaviors. Objective The objectives of our study were to (1) to describe the sociodemographic characteristics associated with health app use in a recent US nationally representative sample; (2) to assess the attitudinal and behavioral predictors of the use of health apps for health promotion; and (3) to examine the association between the use of health-related apps and meeting the recommended guidelines for fruit and vegetable intake and physical activity. Methods Data on users of mobile devices and health apps were analyzed from the National Cancer Institute’s 2015 Health Information National Trends Survey (HINTS), which was designed to provide nationally representative estimates for health information in the United States and is publicly available on the Internet. We used multivariable logistic regression models to assess sociodemographic predictors of mobile device and health app use and examine the associations between app use, intentions to change behavior, and actual behavioral change for fruit and vegetable consumption, physical activity, and weight loss. Results From the 3677 total HINTS respondents, older individuals (45-64 years, odds ratio, OR 0.56, 95% CI 0.47-68; 65+ years, OR 0.19, 95% CI 0.14-0.24), males (OR 0.80, 95% CI 0.66-0.94), and having degree (OR 2.83, 95% CI 2.18-3.70) or less than high school education (OR 0.43, 95% CI 0.24-0.72) were all significantly associated with a reduced likelihood of having adopted health apps. Similarly, both age and education were significant variables for predicting whether a person had adopted a mobile device, especially if that person was a college graduate (OR 3.30). Individuals with apps were significantly more likely to report intentions to improve fruit (63.8% with apps vs 58.5% without apps, P=.01) and vegetable (74.9% vs 64.3%, P<.01) consumption, physical activity (83.0% vs 65.4%, P<.01), and weight loss (83.4% vs 71.8%, P<.01). Individuals with apps were also more likely to meet recommendations for physical activity compared with those without a device or health apps (56.2% with apps vs 47.8% without apps, P<.01). Conclusions The main users of health apps were individuals who were younger, had more education, reported excellent health, and had a higher income. Although differences persist for gender, age, and educational attainment, many individual sociodemographic factors are becoming less potent in influencing engagement with mobile devices and health app use. App use was associated with intentions to change diet and physical activity and meeting physical activity recommendations.
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              Establishing and maintaining long-term human-computer relationships

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                Author and article information

                Contributors
                j.stuber@amsterdamumc.nl
                Journal
                Nutr J
                Nutr J
                Nutrition Journal
                BioMed Central (London )
                1475-2891
                19 May 2020
                19 May 2020
                2020
                : 19
                : 46
                Affiliations
                [1 ]Department of Epidemiology and Biostatistics, Amsterdam Public Health research institute, Amsterdam UMC, VU University Amsterdam, Amsterdam, the Netherlands
                [2 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Upstream Team, www.upstreamteam.nl, Amsterdam UMC, , VU University Amsterdam, ; Amsterdam, the Netherlands
                [3 ]GRID grid.5477.1, ISNI 0000000120346234, Department of Social, Health and Organizational Psychology, , Utrecht University, ; Utrecht, the Netherlands
                [4 ]GRID grid.7177.6, ISNI 0000000084992262, Amsterdam School of Communication Research ASCoR, , University of Amsterdam, ; Amsterdam, the Netherlands
                [5 ]Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
                [6 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Social AI group, department of Computer Science, , VU University Amsterdam, ; Amsterdam, the Netherlands
                [7 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Athena Institute, Faculty of Science, , VU University, ; Amsterdam, The Netherlands
                [8 ]GRID grid.491176.c, ISNI 0000 0004 0395 4926, Netherlands Nutrition Centre (Voedingscentrum), ; The Hague, The Netherlands
                [9 ]Department of Public Health, Amsterdam Public Health research institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
                Author information
                http://orcid.org/0000-0001-7825-018X
                Article
                562
                10.1186/s12937-020-00562-8
                7236937
                32429917
                1fc192fd-535f-43e3-95d1-0e8c1a3b44e5
                © The Author(s) 2020

                Open AccessThis 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
                : 11 December 2019
                : 5 May 2020
                Funding
                Funded by: Hartstichting (NL)
                Award ID: CVON2016-04
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001826, ZonMw;
                Award ID: 531003001
                Award Recipient :
                Categories
                Study Protocol
                Custom metadata
                © The Author(s) 2020

                Nutrition & Dietetics
                cardiovascular disease,type 2 diabetes,food environment,mhealth,ehealth,socioeconomic status

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