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      Impact of the Growing Healthy mHealth Program on Maternal Feeding Practices, Infant Food Preferences, and Satiety Responsiveness: Quasi-Experimental Study

      research-article
      , BSc, BHSc (Hons), PhD 1 , 2 , , , BN, MPH, PhD 2 , 3 , 4 , , BSc, MSc, PhD 2 , 5 , , BA, BSc, Grad Dip Psych, PhD 5 , , BSc (Hons), PhD 5 , , BPhty, BA, MSc, PhD 2 , 6 , , BSc, MPH, PhD 2 , 7 , , BHSc (Hons) 2 , 5 , , BASc, PhD 2 , 8 , , BSc, MPH, PhD 2 , 5
      (Reviewer), (Reviewer), (Reviewer)
      JMIR mHealth and uHealth
      JMIR Publications
      mHealth, obesity, infant, parents, food preferences, appetite, pediatric obesity, feeding behavior, overweight, eating, health promotion

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          Abstract

          Background

          Infancy is an important life stage for obesity prevention efforts. Parents’ infant feeding practices influence the development of infants’ food preferences and eating behaviors and subsequently diet and weight. Mobile health (mHealth) may provide a feasible medium through which to deliver programs to promote healthy infant feeding as it allows low cost and easy access to tailored content.

          Objective

          The objective of this study was to describe the effects of an mHealth intervention on parental feeding practices, infant food preferences, and infant satiety responsiveness.

          Methods

          A quasi-experimental study was conducted with an mHealth intervention group (Growing Healthy) and a nonrandomized comparison group (“Baby's First Food"). The intervention group received access to a free app with age-appropriate push notifications, a website, and an online forum that provided them with evidence-based advice on infant feeding for healthy growth from birth until 9 months of age. Behavior change techniques were selected using the Behaviour Change Wheel framework. Participants in both groups completed three Web-based surveys, first when their infants were less than 3 months old (baseline, T1), then at 6 months (time 2, T2), and 9 months of age (time 3, T3). Surveys included questions on infant feeding practices and beliefs (Infant Feeding Questionnaire, IFQ), satiety responsiveness (Baby Eating Behaviour Questionnaire), and infant’s food exposure and liking. Multivariate linear regression models, estimated using maximum likelihood with bootstrapped standard errors, were fitted to compare continuous outcomes between the intervention groups, with adjustment for relevant covariates. Multivariate logistic regression adjusting for the same covariates was performed for categorical outcomes.

          Results

          A total of 645 parents (Growing Healthy: n=301, Baby's First Food: n=344) met the eligibility criteria and were included in the study, reducing to a sample size of 546 (Growing Healthy: n=234, Baby's First Food: n=312) at T2 and a sample size of 518 (Growing Healthy: n=225, Baby's First Food: n=293) at T3. There were approximately equal numbers of boy and girl infants, and infants were aged less than 3 months at baseline (Growing Healthy: mean 7.0, SD 3.7 weeks; Baby's First Food: mean 7.9, SD 3.8 weeks), with Growing Healthy infants being slightly younger than Baby's First Food infants ( P=.001). All but one (IFQ subscale “concerns about infant overeating or becoming overweight” at T2) of the measured outcomes did not differ between Growing Healthy and Baby's First Food.

          Conclusions

          Although mHealth can be effective in promoting some health behaviors and offers many advantages in health promotion, the results of this study suggest that design and delivery characteristics needed to maximize the impact of mHealth interventions on infant feeding are uncertain. The sensitivity of available measurement tools and differences in baseline characteristics of participants may have also affected the results.

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          Most cited references36

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          Parental influence on eating behavior: conception to adolescence.

          The first years of life mark a time of rapid development and dietary change, as children transition from an exclusive milk diet to a modified adult diet. During these early years, children's learning about food and eating plays a central role in shaping subsequent food choices, diet quality, and weight status. Parents play a powerful role in children's eating behavior, providing both genes and environment for children. For example, they influence children's developing preferences and eating behaviors by making some foods available rather than others, and by acting as models of eating behavior. In addition, parents use feeding practices, which have evolved over thousands of years, to promote patterns of food intake necessary for children's growth and health. However in current eating environments, characterized by too much inexpensive palatable, energy dense food, these traditional feeding practices can promote overeating and weight gain. To meet the challenge of promoting healthy weight in children in the current eating environment, parents need guidance regarding alternatives to traditional feeding practices.
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            The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes

            Background A primary focus of self-care interventions for chronic illness is the encouragement of an individual's behavior change necessitating knowledge sharing, education, and understanding of the condition. The use of the Internet to deliver Web-based interventions to patients is increasing rapidly. In a 7-year period (1996 to 2003), there was a 12-fold increase in MEDLINE citations for “Web-based therapies.” The use and effectiveness of Web-based interventions to encourage an individual's change in behavior compared to non-Web-based interventions have not been substantially reviewed. Objective This meta-analysis was undertaken to provide further information on patient/client knowledge and behavioral change outcomes after Web-based interventions as compared to outcomes seen after implementation of non-Web-based interventions. Methods The MEDLINE, CINAHL, Cochrane Library, EMBASE, ERIC, and PSYCHInfo databases were searched for relevant citations between the years 1996 and 2003. Identified articles were retrieved, reviewed, and assessed according to established criteria for quality and inclusion/exclusion in the study. Twenty-two articles were deemed appropriate for the study and selected for analysis. Effect sizes were calculated to ascertain a standardized difference between the intervention (Web-based) and control (non-Web-based) groups by applying the appropriate meta-analytic technique. Homogeneity analysis, forest plot review, and sensitivity analyses were performed to ascertain the comparability of the studies. Results Aggregation of participant data revealed a total of 11,754 participants (5,841 women and 5,729 men). The average age of participants was 41.5 years. In those studies reporting attrition rates, the average drop out rate was 21% for both the intervention and control groups. For the five Web-based studies that reported usage statistics, time spent/session/person ranged from 4.5 to 45 minutes. Session logons/person/week ranged from 2.6 logons/person over 32 weeks to 1008 logons/person over 36 weeks. The intervention designs included one-time Web-participant health outcome studies compared to non-Web participant health outcomes, self-paced interventions, and longitudinal, repeated measure intervention studies. Longitudinal studies ranged from 3 weeks to 78 weeks in duration. The effect sizes for the studied outcomes ranged from -.01 to .75. Broad variability in the focus of the studied outcomes precluded the calculation of an overall effect size for the compared outcome variables in the Web-based compared to the non-Web-based interventions. Homogeneity statistic estimation also revealed widely differing study parameters (Qw16 = 49.993, P ≤ .001). There was no significant difference between study length and effect size. Sixteen of the 17 studied effect outcomes revealed improved knowledge and/or improved behavioral outcomes for participants using the Web-based interventions. Five studies provided group information to compare the validity of Web-based vs. non-Web-based instruments using one-time cross-sectional studies. These studies revealed effect sizes ranging from -.25 to +.29. Homogeneity statistic estimation again revealed widely differing study parameters (Qw4 = 18.238, P ≤ .001). Conclusions The effect size comparisons in the use of Web-based interventions compared to non-Web-based interventions showed an improvement in outcomes for individuals using Web-based interventions to achieve the specified knowledge and/or behavior change for the studied outcome variables. These outcomes included increased exercise time, increased knowledge of nutritional status, increased knowledge of asthma treatment, increased participation in healthcare, slower health decline, improved body shape perception, and 18-month weight loss maintenance.
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              Determinants of fruit and vegetable consumption among children and adolescents: a review of the literature. Part I: quantitative studies

              Background In order to more effectively promote fruit and vegetable intake among children and adolescents, insight into determinants of intake is necessary. We conducted a review of the literature for potential determinants of fruit and vegetable intake in children and adolescents. Methods Papers were identified from Medline and PsycINFO by using all combinations of the search terms: "fruit(s) or vegetable(s)" and "children or adolescents". Quantitative research examining determinants of fruit and/or vegetable intake among children and adolescents aged 6–18 years were included. The selection and review process was conducted according to a four-step protocol resulting in information on country, population, design, methodology, theoretical basis, instrument used for measuring intake, statistical analysis, included independent variables, and effect sizes. Results Ninety-eight papers were included. A large number of potential determinants have been studied among children and adolescents. However, for many presumed determinants convincing evidence is lacking, mostly because of paucity of studies. The determinants best supported by evidence are: age, gender, socio-economic position, preferences, parental intake, and home availability/accessibility. Girls and younger children tend to have a higher or more frequent intake than boys and older children. Socio-economic position, preferences, parental intake, and home availability/accessibility are all consistently positively associated with intake. Conclusion The determinants most consistently supported by evidence are gender, age, socio-economic position, preferences, parental intake and home availability/accessibility. There is a need for internationally comparative, longitudinal, theory-based and multi-level studies taking both personal and environmental factors into account. This paper is published as part of the special Pro Children series in the International Journal of Behavioral Nutrition and Physical Activity. Please see [] for the relevant editorial.
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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 2018
                25 April 2018
                : 6
                : 4
                : e77
                Affiliations
                [1] 1 Centre for Advanced Sensory Science School of Exercise and Nutrition Sciences, Faculty of Health Deakin University Burwood Australia
                [2] 2 Centre for Obesity Management and Prevention Research Excellence in Primary Health Care Sydney Australia
                [3] 3 Sydney Nursing School The University of Sydney Sydney Australia
                [4] 4 Sydney Local Health District Sydney Australia
                [5] 5 Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University Geelong Australia
                [6] 6 The Boden Institute of Obesity Nutrition Exercise & Eating Disorders Charles Perkins Centre University of Sydney Sydney Australia
                [7] 7 Health Promotion Unit Sydney Local Health District and University of Sydney Sydney Australia
                [8] 8 Department of Accounting and Data Analytics, La Trobe Business School College of Arts, Social Sciences and Commerce La Trobe University Melbourne Australia
                Author notes
                Corresponding Author: Catherine Georgina Russell georgie.russell@ 123456deakin.edu.au
                Author information
                http://orcid.org/0000-0002-0848-2724
                http://orcid.org/0000-0001-9879-4969
                http://orcid.org/0000-0003-4328-1116
                http://orcid.org/0000-0003-4014-0705
                http://orcid.org/0000-0002-4151-3502
                http://orcid.org/0000-0002-7876-6722
                http://orcid.org/0000-0002-7228-8993
                http://orcid.org/0000-0002-4618-1068
                http://orcid.org/0000-0003-4688-7674
                http://orcid.org/0000-0002-4499-3396
                Article
                v6i4e77
                10.2196/mhealth.9303
                5943630
                29695373
                d910c7ba-dfff-4f64-bd39-74274dfae42e
                ©Catherine Georgina Russell, Elizabeth Denney-Wilson, Rachel A Laws, Gavin Abbott, Miaobing Zheng, Sharyn J Lymer, Sarah Taki, Eloise-Kate V Litterbach, Kok-Leong Ong, Karen J Campbell. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 25.04.2018.

                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
                : 29 October 2017
                : 6 December 2017
                : 22 December 2017
                : 22 December 2017
                Categories
                Original Paper
                Original Paper

                mhealth,obesity,infant,parents,food preferences,appetite,pediatric obesity,feeding behavior,overweight,eating,health promotion

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