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      The effectiveness of app‐based mobile interventions on nutrition behaviours and nutrition‐related health outcomes: A systematic review and meta‐analysis

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          Summary

          A systematic review and meta‐analysis were conducted to assess the effectiveness of app‐based mobile interventions for improving nutrition behaviours and nutrition‐related health outcomes, including obesity indices (eg, body mass index [BMI]) and clinical parameters (eg, blood lipids). Seven databases were searched for studies published between 2006 and 2017. Forty‐one of 10 132 identified records were included, comprising 6348 participants and 373 outcomes with sample sizes ranging from 10 to 833, including 27 randomized controlled trials (RCTs). A beneficial effect of app‐based mobile interventions was identified for improving nutrition behaviours ( g = 0.19; CI, 0.06‐0.32, P = .004) and nutrition‐related health outcomes ( g = 0.23; CI, 0.11‐0.36, P < .001), including positive effects on obesity indices ( g = 0.30; CI, 0.15‐0.45, P < .001), blood pressure ( g = 0.21; CI, 0.01‐0.42, P = .043), and blood lipids ( g = 0.15; CI, 0.03‐0.28, P = .018). Most interventions were composed of four behaviour change technique (BCT) clusters, namely, “goals/planning,” “feedback/monitoring,” “shaping knowledge,” and “social support.” Moderating effects including study design, type of app (commercial/research app), sample characteristics (clinical/non‐clinical sample), and intervention characteristics were not statistically significant. The inclusion of additional treatment components besides the app or the number or type of BCTs implemented did not moderate the observed effectiveness, which underscores the potential of app‐based mobile interventions for implementing effective and feasible interventions operating at scale for fighting the obesity epidemic in a broad spectrum of the population.

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          Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013.

          In 2010, overweight and obesity were estimated to cause 3·4 million deaths, 3·9% of years of life lost, and 3·8% of disability-adjusted life-years (DALYs) worldwide. The rise in obesity has led to widespread calls for regular monitoring of changes in overweight and obesity prevalence in all populations. Comparable, up-to-date information about levels and trends is essential to quantify population health effects and to prompt decision makers to prioritise action. We estimate the global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013. We systematically identified surveys, reports, and published studies (n=1769) that included data for height and weight, both through physical measurements and self-reports. We used mixed effects linear regression to correct for bias in self-reports. We obtained data for prevalence of obesity and overweight by age, sex, country, and year (n=19,244) with a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs). Worldwide, the proportion of adults with a body-mass index (BMI) of 25 kg/m(2) or greater increased between 1980 and 2013 from 28·8% (95% UI 28·4-29·3) to 36·9% (36·3-37·4) in men, and from 29·8% (29·3-30·2) to 38·0% (37·5-38·5) in women. Prevalence has increased substantially in children and adolescents in developed countries; 23·8% (22·9-24·7) of boys and 22·6% (21·7-23·6) of girls were overweight or obese in 2013. The prevalence of overweight and obesity has also increased in children and adolescents in developing countries, from 8·1% (7·7-8·6) to 12·9% (12·3-13·5) in 2013 for boys and from 8·4% (8·1-8·8) to 13·4% (13·0-13·9) in girls. In adults, estimated prevalence of obesity exceeded 50% in men in Tonga and in women in Kuwait, Kiribati, Federated States of Micronesia, Libya, Qatar, Tonga, and Samoa. Since 2006, the increase in adult obesity in developed countries has slowed down. Because of the established health risks and substantial increases in prevalence, obesity has become a major global health challenge. Not only is obesity increasing, but no national success stories have been reported in the past 33 years. Urgent global action and leadership is needed to help countries to more effectively intervene. Bill & Melinda Gates Foundation. Copyright © 2014 Elsevier Ltd. All rights reserved.
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            A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

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              Effect size, confidence interval and statistical significance: a practical guide for biologists.

              Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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                Author and article information

                Contributors
                Britta.renner@uni-konstanz.de
                Journal
                Obes Rev
                Obes Rev
                10.1111/(ISSN)1467-789X
                OBR
                Obesity Reviews
                John Wiley and Sons Inc. (Hoboken )
                1467-7881
                1467-789X
                28 July 2019
                October 2019
                : 20
                : 10 ( doiID: 10.1111/obr.v20.10 )
                : 1465-1484
                Affiliations
                [ 1 ] Department of Psychology, Psychological Assessment and Health Psychology University of Konstanz Konstanz Germany
                [ 2 ] Department of Epidemiology German Institute of Human Nutrition Nuthetal Germany
                [ 3 ] Department of Psychology, General and Biological Psychology University of Konstanz Konstanz Germany
                Author notes
                [*] [* ] Correspondence

                Britta Renner, Department of Psychology, Psychological Assessment and Health Psychology, University of Konstanz, PO Box 47, Konstanz D‐78457, Germany.

                Email: Britta.renner@ 123456uni-konstanz.de

                Author information
                https://orcid.org/0000-0002-4103-0498
                https://orcid.org/0000-0002-9355-9289
                https://orcid.org/0000-0002-1725-9129
                https://orcid.org/0000-0001-8385-2839
                Article
                OBR12903 OBR-01-19-3772.R1
                10.1111/obr.12903
                6852183
                31353783
                bb422ec1-0fb4-434b-8d4d-35140188d610
                © 2019 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 21 January 2019
                : 30 April 2019
                : 06 May 2019
                Page count
                Figures: 6, Tables: 1, Pages: 20, Words: 9699
                Funding
                Funded by: German Research Foundation , open-funder-registry 10.13039/501100001659;
                Award ID: 2374
                Funded by: Federal Ministry of Education and Research , open-funder-registry 10.13039/501100002347;
                Award ID: 01EL1820A
                Categories
                Obesity/Behavior Intervention
                Obesity/Behavior Intervention
                Custom metadata
                2.0
                October 2019
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.1 mode:remove_FC converted:13.11.2019

                Medicine
                bct,diet,intervention,m‐health,mobile apps,nutrition behaviour,nutritional outcomes,obesity
                Medicine
                bct, diet, intervention, m‐health, mobile apps, nutrition behaviour, nutritional outcomes, obesity

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