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      Use of Fitness and Nutrition Apps: Associations With Body Mass Index, Snacking, and Drinking Habits in Adolescents

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          Abstract

          Background

          Efforts to improve snacking and drinking habits are needed to promote a healthy body mass index (BMI) in adolescents. Although commercial fitness and nutrition mobile phone apps are widely used, little is known regarding their potential to improve health behaviors, especially in adolescents. In addition, evidence on the mechanisms through which such fitness and nutrition apps influence behavior is lacking.

          Objectives

          This study assessed whether the use of commercial fitness or nutrition apps was associated with a lower BMI and healthier snacking and drinking habits in adolescents. Additionally, it explored if perceived behavioral control to eat healthy; attitudes to eat healthy for the good taste of healthy foods, for overall health or for appearance; social norm on healthy eating and social support to eat healthy mediated the associations between the frequency of use of fitness or nutrition apps and BMI, the healthy snack, and beverage ratio.

          Methods

          Cross-sectional self-reported data on snack and beverage consumption, healthy eating determinants, and fitness and nutrition app use of adolescents (N=889; mean age 14.7 years, SD 0.8; 54.8% [481/878] boys; 18.1% [145/803] overweight) were collected in a representative sample of 20 schools in Flanders, Belgium. Height and weight were measured by the researchers. The healthy snack ratio and the healthy beverage ratio were calculated as follows: gram healthy snacks or beverages/(gram healthy snacks or beverages+gram unhealthy snacks or beverages)×100. Multilevel regression and structural equation modeling were used to analyze the proposed associations and to explore multiple mediation.

          Results

          A total of 27.6% (245/889) of the adolescents used fitness, nutrition apps or both. Frequency of using nutrition apps was positively associated with a higher healthy beverage ratio ( b=2.96 [1.11], P=.008) and a higher body mass index z-scores (zBMI; b=0.13 [0.05], P=.008. A significant interaction was found between the frequency of using nutrition and for the zBMI ( b=−0.03 [0.02], P=.04) and the healthy snack ratio ( b=−0.84 [0.37], P=.03). Attitude to eat healthy for appearance mediated both the fitness app use frequency-zBMI ( a × b=0.02 [0.01], P=.02) and the nutrition app use frequency-zBMI ( a × b=0.04 [0.01], P=.001) associations. No mediation was observed for the associations between the frequency of use of fitness or nutrition apps and the healthy snack or beverage ratio.

          Conclusions

          Commercial fitness and nutrition apps show some association with healthier eating behaviors and BMI in adolescents. However, effective behavior change techniques should be included to affect key determinants of healthy eating.

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          Most cited references41

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          Apps to promote physical activity among adults: a review and content analysis

          Background In May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone applications (apps) categorized as Health and Fitness, respectively. The quality of these apps, in terms of applying established health behavior change techniques, remains unclear. Methods The study sample was identified through systematic searches in iTunes and Google Play. Search terms were based on Boolean logic and included AND combinations for physical activity, healthy lifestyle, exercise, fitness, coach, assistant, motivation, and support. Sixty-four apps were downloaded, reviewed, and rated based on the taxonomy of behavior change techniques used in the interventions. Mean and ranges were calculated for the number of observed behavior change techniques. Using nonparametric tests, we compared the number of techniques observed in free and paid apps and in iTunes and Google Play. Results On average, the reviewed apps included 5 behavior change techniques (range 2–8). Techniques such as self-monitoring, providing feedback on performance, and goal-setting were used most frequently, whereas some techniques such as motivational interviewing, stress management, relapse prevention, self-talk, role models, and prompted barrier identification were not. No differences in the number of behavior change techniques between free and paid apps, or between the app stores were found. Conclusions The present study demonstrated that apps promoting physical activity applied an average of 5 out of 23 possible behavior change techniques. This number was not different for paid and free apps or between app stores. The most frequently used behavior change techniques in apps were similar to those most frequently used in other types of physical activity promotion interventions.
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            Changes in beverage intake between 1977 and 2001.

            To examine American beverage consumption trends and causes. Nationally representative data from the 1977-1978 Nationwide Food Consumption Survey, the 1989-1991 and 1994-1996 (also for children aged 2 to 9 years in 1998) Continuing Surveys of Food Intake by Individuals (CSFII), and 1999-2001 National Health and Nutrition Examination Survey were used in this study. The sample consisted of 73,345 individuals, aged >or=2 years. For each survey year, the percentage of total energy intake from meals and snacks was calculated separately for respondents aged 2 to 18 years, 19 to 39, 40 to 59, and >or=60. The percentage of energy intake by location (at home consumption or preparation, vending, store eaten out, restaurant/fast food, and school), as well as for specific beverages was computed separately for all age groups. The proportion consumed, mean portion size, and number of servings were calculated. For all age groups, sweetened beverage consumption increased and milk consumption decreased. Overall, energy intake from sweetened beverages increased 135% and was reduced by 38% from milk, with a 278 total calorie increase. These trends were associated with increased proportions of Americans consuming larger portions, more servings per day of sweetened beverage, and reductions in these same measures for milk. There is little research that has focused on the beneficial impacts of reduced soft drink and fruit drink intake. This would seem to be one of the simpler ways to reduce obesity in the United States.
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              Mobile applications for weight management: theory-based content analysis.

              The use of smartphone applications (apps) to assist with weight management is increasingly prevalent, but the quality of these apps is not well characterized. The goal of the study was to evaluate diet/nutrition and anthropometric tracking apps based on incorporation of features consistent with theories of behavior change. A comparative, descriptive assessment was conducted of the top-rated free apps in the Health and Fitness category available in the iTunes App Store. Health and Fitness apps (N=200) were evaluated using predetermined inclusion/exclusion criteria and categorized based on commonality in functionality, features, and developer description. Four researchers then evaluated the two most popular apps in each category using two instruments: one based on traditional behavioral theory (score range: 0-100) and the other on the Fogg Behavioral Model (score range: 0-6). Data collection and analysis occurred in November 2012. Eligible apps (n=23) were divided into five categories: (1) diet tracking; (2) healthy cooking; (3) weight/anthropometric tracking; (4) grocery decision making; and (5) restaurant decision making. The mean behavioral theory score was 8.1 (SD=4.2); the mean persuasive technology score was 1.9 (SD=1.7). The top-rated app on both scales was Lose It! by Fitnow Inc. All apps received low overall scores for inclusion of behavioral theory-based strategies. © 2013 American Journal of Preventive Medicine.
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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
                April 2017
                25 April 2017
                : 5
                : 4
                : e58
                Affiliations
                [1] 1Food Chemistry and Human Nutrition Department of Food safety and Food quality University of Ghent GentBelgium
                [2] 2School for Mass Communication Research Faculty of Social Sciences KU Leuven LeuvenBelgium
                [3] 3Clinical Developmental Psychology Department of Developmental, Personality and Social psychology University of Ghent GentBelgium
                [4] 4Health Promotion and Education Departement of Public Health University of Ghent GentBelgium
                [5] 5Physical activity, Nutrition and Health Faculty of Physical Education and Physical Therapy Vrije Universiteit Brussel BrusselBelgium
                [6] 6Nutrition and Food Safety Departement of Public Health University of Ghent GentBelgium
                Author notes
                Corresponding Author: Nathalie De Cock nathalieL.decock@ 123456ugent.be
                Author information
                http://orcid.org/0000-0002-0053-0269
                http://orcid.org/0000-0001-5890-0106
                http://orcid.org/0000-0002-1389-8855
                http://orcid.org/0000-0002-0530-7947
                http://orcid.org/0000-0002-5082-7175
                http://orcid.org/0000-0002-5518-2855
                http://orcid.org/0000-0003-2909-6010
                http://orcid.org/0000-0002-1064-0525
                http://orcid.org/0000-0003-4141-5432
                http://orcid.org/0000-0003-2458-287X
                http://orcid.org/0000-0003-0428-092X
                http://orcid.org/0000-0002-0504-2205
                http://orcid.org/0000-0002-6468-6107
                http://orcid.org/0000-0002-8535-0215
                Article
                v5i4e58
                10.2196/mhealth.6005
                5424128
                28442455
                28841359-9250-4b2d-b0aa-09edbce70fb9
                ©Nathalie De Cock, Jolien Vangeel, Carl Lachat, Kathleen Beullens, Leentje Vervoort, Lien Goossens, Lea Maes, Benedicte Deforche, Stefaan De Henauw, Caroline Braet, Steven Eggermont, Patrick Kolsteren, John Van Camp, Wendy Van Lippevelde. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 25.04.2017.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), 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
                : 27 May 2016
                : 25 August 2016
                : 19 October 2016
                : 29 January 2017
                Categories
                Original Paper
                Original Paper

                mhealth,adolescents,snacks,beverages,body mass index
                mhealth, adolescents, snacks, beverages, body mass index

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