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      A Focused Review of Smartphone Diet-Tracking Apps: Usability, Functionality, Coherence With Behavior Change Theory, and Comparative Validity of Nutrient Intake and Energy Estimates

      research-article
      , BA 1 , 2 , , BS 3 , , BSc (Hons), RD, MPH, PhD 4 , , AB, MS, PhD 5 ,
      (Reviewer), (Reviewer), (Reviewer), (Reviewer)
      JMIR mHealth and uHealth
      JMIR Publications
      diet, nutrition assessment, behavior and behavior mechanisms

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          Abstract

          Background

          Smartphone diet-tracking apps may help individuals lose weight, manage chronic conditions, and understand dietary patterns; however, the usabilities and functionalities of these apps have not been well studied.

          Objective

          The aim of this study was to review the usability of current iPhone operating system (iOS) and Android diet-tracking apps, the degree to which app features align with behavior change constructs, and to assess variations between apps in nutrient coding.

          Methods

          The top 7 diet-tracking apps were identified from the iOS iTunes and Android Play online stores, downloaded and used over a 2-week period. Each app was independently scored by researchers using the System Usability Scale (SUS), and features were compared with the domains in an integrated behavior change theory framework: the Theoretical Domains Framework. An estimated 3-day food diary was completed using each app, and food items were entered into the United States Department of Agriculture (USDA) Food Composition Databases to evaluate their differences in nutrient data against the USDA reference.

          Results

          Of the apps that were reviewed, LifeSum had the highest average SUS score of 89.2, whereas MyDietCoach had the lowest SUS score of 46.7. Some variations in features were noted between Android and iOS versions of the same apps, mainly for MyDietCoach, which affected the SUS score. App features varied considerably, yet all of the apps had features consistent with Beliefs about Capabilities and thus have the potential to promote self-efficacy by helping individuals track their diet and progress toward goals. None of the apps allowed for tracking of emotional factors that may be associated with diet patterns. The presence of behavior change domain features tended to be weakly correlated with greater usability, with R 2 ranging from 0 to .396. The exception to this was features related to the Reinforcement domain, which were correlated with less usability. Comparing the apps with the USDA reference for a 3-day diet, the average differences were 1.4% for calories, 1.0% for carbohydrates, 10.4% for protein, and −6.5% for fat.

          Conclusions

          Almost all reviewed diet-tracking apps scored well with respect to usability, used a variety of behavior change constructs, and accurately coded calories and carbohydrates, allowing them to play a potential role in dietary intervention studies.

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          Most cited references28

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          The contribution of expanding portion sizes to the US obesity epidemic.

          Because larger food portions could be contributing to the increasing prevalence of overweight and obesity, this study was designed to weigh samples of marketplace foods, identify historical changes in the sizes of those foods, and compare current portions with federal standards. We obtained information about current portions from manufacturers or from direct weighing; we obtained information about past portions from manufacturers or contemporary publications. Marketplace food portions have increased in size and now exceed federal standards. Portion sizes began to grow in the 1970s, rose sharply in the 1980s, and have continued in parallel with increasing body weights. Because energy content increases with portion size, educational and other public health efforts to address obesity should focus on the need for people to consume smaller portions.
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            Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort.

            The aim of this study was to examine the relation between carbohydrate-related dietary factors, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort. We examined cross-sectional associations between carbohydrate-related dietary factors, insulin resistance, and the prevalence of the metabolic syndrome in 2,834 subjects at the fifth examination (1991-1995) of the Framingham Offspring Study. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated using the following formula (fasting plasma insulin x plasma glucose)/22.5. The metabolic syndrome was defined using the National Cholesterol Education Program criteria. After adjustment for potential confounding variables, intakes of total dietary fiber, cereal fiber, fruit fiber, and whole grains were inversely associated, whereas glycemic index and glycemic load were positively associated with HOMA-IR. The prevalence of the metabolic syndrome was significantly lower among those in the highest quintile of cereal fiber (odds ratio [OR] 0.62; 95% CI 0.45-0.86) and whole-grain (0.67; 0.48-0.91) intakes relative to those in the lowest quintile category after adjustment for confounding lifestyle and dietary factors. Conversely, the prevalence of the metabolic syndrome was significantly higher among individuals in the highest relative to the lowest quintile category of glycemic index (1.41; 1.04-1.91). Total carbohydrate, dietary fiber, fruit fiber, vegetable fiber, legume fiber, glycemic load, and refined grain intakes were not associated with prevalence of the metabolic syndrome. Whole-grain intake, largely attributed to the cereal fiber, is inversely associated with HOMA-IR and a lower prevalence of the metabolic syndrome. Dietary glycemic index is positively associated with HOMA-IR and prevalence of the metabolic syndrome. Given that both a high cereal fiber content and lower glycemic index are attributes of whole-grain foods, recommendation to increase whole-grain intake may reduce the risk of developing the metabolic syndrome.
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              Gamification: What It Is and Why It Matters to Digital Health Behavior Change Developers

              This editorial provides a behavioral science view on gamification and health behavior change, describes its principles and mechanisms, and reviews some of the evidence for its efficacy. Furthermore, this editorial explores the relation between gamification and behavior change frameworks used in the health sciences and shows how gamification principles are closely related to principles that have been proven to work in health behavior change technology. Finally, this editorial provides criteria that can be used to assess when gamification provides a potentially promising framework for digital health interventions.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                May 2019
                17 May 2019
                : 7
                : 5
                : e9232
                Affiliations
                [1 ] Global Burden of Disease, Institute for Health Metrics and Evaluation University of Washington Seattle, WA United States
                [2 ] Paul G Allen School of Computer Science & Engineering University of Washington Seattle, WA United States
                [3 ] Department of Kinesiology and Nutrition University of Illinois, Chicago Chicago, IL United States
                [4 ] Department of Medicine Stanford Prevention Research Center Stanford University Stanford, CA United States
                [5 ] Department of Environmental & Occupational Health Sciences University of Washington Seattle, WA United States
                Author notes
                Corresponding Author: Edmund Seto eseto@ 123456uw.edu
                Author information
                http://orcid.org/0000-0002-0981-8090
                http://orcid.org/0000-0002-5856-9115
                http://orcid.org/0000-0002-7982-746X
                http://orcid.org/0000-0001-5462-0772
                http://orcid.org/0000-0003-4058-0313
                Article
                v7i5e9232
                10.2196/mhealth.9232
                6543803
                31102369
                467327aa-309b-4166-af38-e56730c7c76c
                ©Giannina Ferrara, Jenna Kim, Shuhao Lin, Jenna Hua, Edmund Seto. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 17.05.2019.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/.as well as this copyright and license information must be included.

                History
                : 9 December 2017
                : 15 March 2018
                : 10 August 2018
                : 10 April 2019
                Categories
                Original Paper
                Original Paper

                diet,nutrition assessment,behavior and behavior mechanisms

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