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      A Brief Tool to Assess Image-Based Dietary Records and Guide Nutrition Counselling Among Pregnant Women: An Evaluation

      research-article
      , B Nutr & Diet 1 , 2 , 3 , , PhD 1 , 2 , , PhD 4 , , PhD 3 , 4 , 5 , 6 , , PhD 1 , 2 ,
      (Reviewer), (Reviewer)
      JMIR mHealth and uHealth
      JMIR Publications
      nutrition assessment, pregnancy, telehealth, image-based dietary records

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          Abstract

          Background

          Dietitians ideally should provide personally tailored nutrition advice to pregnant women. Provision is hampered by a lack of appropriate tools for nutrition assessment and counselling in practice settings. Smartphone technology, through the use of image-based dietary records, can address limitations of traditional methods of recording dietary intake. Feedback on these records can then be provided by the dietitian via smartphone. Efficacy and validity of these methods requires examination.

          Objective

          The aims of the Australian Diet Bytes and Baby Bumps study, which used image-based dietary records and a purpose-built brief Selected Nutrient and Diet Quality (SNaQ) tool to provide tailored nutrition advice to pregnant women, were to assess relative validity of the SNaQ tool for analyzing dietary intake compared with nutrient analysis software, to describe the nutritional intake adequacy of pregnant participants, and to assess acceptability of dietary feedback via smartphone.

          Methods

          Eligible women used a smartphone app to record everything they consumed over 3 nonconsecutive days. Records consisted of an image of the food or drink item placed next to a fiducial marker, with a voice or text description, or both, providing additional detail. We used the SNaQ tool to analyze participants’ intake of daily food group servings and selected key micronutrients for pregnancy relative to Australian guideline recommendations. A visual reference guide consisting of images of foods and drinks in standard serving sizes assisted the dietitian with quantification. Feedback on participants’ diets was provided via 2 methods: (1) a short video summary sent to participants’ smartphones, and (2) a follow-up telephone consultation with a dietitian. Agreement between dietary intake assessment using the SNaQ tool and nutrient analysis software was evaluated using Spearman rank correlation and Cohen kappa.

          Results

          We enrolled 27 women (median age 28.8 years, 8 Indigenous Australians, 15 primiparas), of whom 25 completed the image-based dietary record. Median intakes of grains, vegetables, fruit, meat, and dairy were below recommendations. Median (interquartile range) intake of energy-dense, nutrient-poor foods was 3.5 (2.4-3.9) servings/day and exceeded recommendations (0-2.5 servings/day). Positive correlations between the SNaQ tool and nutrient analysis software were observed for energy (ρ=.898, P<.001) and all selected micronutrients (iron, calcium, zinc, folate, and iodine, ρ range .510-.955, all P<.05), both with and without vitamin and mineral supplements included in the analysis. Cohen kappa showed moderate to substantial agreement for selected micronutrients when supplements were included (kappa range .488-.803, all P ≤.001) and for calcium, iodine, and zinc when excluded (kappa range .554-.632, all P<.001). A total of 17 women reported changing their diet as a result of the personalized nutrition advice.

          Conclusions

          The SNaQ tool demonstrated acceptable validity for assessing adequacy of key pregnancy nutrient intakes and preliminary evidence of utility to support dietitians in providing women with personalized advice to optimize nutrition during pregnancy.

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          Most cited references45

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          Recent evidence demonstrates important maternal effects on an offspring's risk of developing metabolic disease. These effects extend across the full range of maternal environments and partly involve epigenetic mechanisms. The maternal effects can be explained in evolutionary terms, and there is some evidence for their transmission into succeeding generations. Unbalanced maternal diet or body composition, ranging from poor to rich environments, adversely influences the offspring's response to later challenges such as an obesogenic diet or physical inactivity, increasing the risk of disease. Adopting a life course approach that takes into account intergenerational effects has important implications for prevention of non-communicable diseases, particularly in populations undergoing rapid economic transition. Copyright 2010. Published by Elsevier Ltd.
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              Validity of the Remote Food Photography Method (RFPM) for estimating energy and nutrient intake in near real-time.

              Two studies are reported; a pilot study to demonstrate feasibility followed by a larger validity study. Study 1's objective was to test the effect of two ecological momentary assessment (EMA) approaches that varied in intensity on the validity/accuracy of estimating energy intake (EI) with the Remote Food Photography Method (RFPM) over 6 days in free-living conditions. When using the RFPM, Smartphones are used to capture images of food selection and plate waste and to send the images to a server for food intake estimation. Consistent with EMA, prompts are sent to the Smartphones reminding participants to capture food images. During Study 1, EI estimated with the RFPM and the gold standard, doubly labeled water (DLW), were compared. Participants were assigned to receive Standard EMA Prompts (n = 24) or Customized Prompts (n = 16) (the latter received more reminders delivered at personalized meal times). The RFPM differed significantly from DLW at estimating EI when Standard (mean ± s.d. = -895 ± 770 kcal/day, P < 0.0001), but not Customized Prompts (-270 ± 748 kcal/day, P = 0.22) were used. Error (EI from the RFPM minus that from DLW) was significantly smaller with Customized vs. Standard Prompts. The objectives of Study 2 included testing the RFPM's ability to accurately estimate EI in free-living adults (N = 50) over 6 days, and energy and nutrient intake in laboratory-based meals. The RFPM did not differ significantly from DLW at estimating free-living EI (-152 ± 694 kcal/day, P = 0.16). During laboratory-based meals, estimating energy and macronutrient intake with the RFPM did not differ significantly compared to directly weighed intake.
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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
                Oct-Dec 2016
                04 November 2016
                : 4
                : 4
                : e123
                Affiliations
                [1] 1School of Health Sciences Faculty of Health and Medicine University of Newcastle CallaghanAustralia
                [2] 2Priority Research Centre in Physical Activity and Nutrition Faculty of Health and Medicine University of Newcastle CallaghanAustralia
                [3] 3Gomeroi gaaynggal Centre Faculty of Health and Medicine University of Newcastle TamworthAustralia
                [4] 4Department of Rural Health Faculty of Health and Medicine University of Newcastle TamworthAustralia
                [5] 5Priority Research Centre in Reproduction Faculty of Health and Medicine University of Newcastle CallaghanAustralia
                [6] 6Mothers and Babies Research Centre Faculty of Health and Medicine University of Newcastle New Lambton HeightsAustralia
                Author notes
                Corresponding Author: Megan E Rollo megan.rollo@ 123456newcastle.edu.au
                Author information
                http://orcid.org/0000-0001-6939-1781
                http://orcid.org/0000-0003-3298-756X
                http://orcid.org/0000-0002-4340-2320
                http://orcid.org/0000-0002-6016-3464
                http://orcid.org/0000-0003-1303-2063
                Article
                v4i4e123
                10.2196/mhealth.6469
                5116101
                27815234
                3a66961a-7251-41d6-a028-b15fe03f7f7b
                ©Amy M Ashman, Clare E Collins, Leanne J Brown, Kym M Rae, Megan E Rollo. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.11.2016.

                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
                : 9 August 2016
                : 1 September 2016
                : 22 September 2016
                : 14 October 2016
                Categories
                Original Paper
                Original Paper

                nutrition assessment,pregnancy,telehealth,image-based dietary records

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