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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.
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.
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.
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,
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.
Dietitians can assess individual dietary needs and provide advice to clients to optimize their nutritional status [
Traditional prospective methods of dietary assessment, including weighed or estimated food records, require the recording of all food and drinks consumed. These methods can capture day-to-day variation in diets and are used commonly in research [
Manual analysis of food records by dietitians or other trained individuals is often required to translate reported food intakes into nutrients and food groups. This analysis is usually undertaken using food composition tables, often embedded in food analysis software. Food composition tables provide detailed information on nutrient composition of foods and drinks, giving determined values for quantities of energy, macronutrients (carbohydrate, protein, and fat), micronutrients (vitamins and minerals), and other food components, such as fiber [
Once dietary intake is analyzed, nutrient intakes can be compared with national recommendations. In Australia, the
Innovative dietary assessment methods can address some of the limitations associated with current methods in order to improve the quality of data collected and ease of analysis. Image-based dietary records are a novel method for food and nutrient intake assessment [
Previous methods of image-based dietary assessment have been examined in healthy adult [
The Diet Bytes and Baby Bumps (DBBB) study used image-based dietary records, captured via smartphone, in pregnant Indigenous and non-Indigenous women. The DBBB study sought to assess intake of AGTHE core and energy-dense, nutrient-poor food groups, total energy, and selected micronutrients, and to provide personalized feedback to these women via their smartphones, in combination with consultation with a dietitian.
The aims of this analysis were to evaluate the use of a brief approach to dietary analysis using a purpose-built Selected Nutrient and Diet Quality (SNaQ) tool to (1) assess nutrient intakes of pregnant women in the DBBB study, (2) assess the validity of the SNaQ tool for nutrient assessment relative to analysis using nutrient analysis software, and (3) assess the acceptability of SNaQ to pregnant women for provision of feedback on dietary intake.
The DBBB study was approved by the Aboriginal Health and Medical Research Council Ethics committee (962/13), Hunter New England Human Research Ethics Committee (13/06/19/4.04), and the University of Newcastle Human Research Ethics Committee (H-2013-0185). The study was conducted in two locations in New South Wales (NSW), Australia: Newcastle, the second largest city in NSW, and Tamworth, a regional inland NSW town.
We recruited participants via promotional fliers at hospital antenatal and general practitioner clinics and the University of Newcastle, through social media (including parenting sites), and through direct contact with pregnant women at antenatal clinics. In Tamworth, participants were also invited to participate through the Gomeroi gaaynggal Centre [
The study ran for 12 weeks (
We modelled the method of capturing dietary intake using image-based records on our previous validated method in adults with type 2 diabetes [
Diet Bytes and Baby Bumps study protocol.
Example of an image-based dietary record in the Diet Bytes and Baby Bumps study, consisting of image, fiducial marker, and audio description of the food and drink items.
The SNaQ tool was developed as a brief tool to analyze participants’ dietary intake relative to AGTHE daily servings of core and energy-dense, nutrient-poor foods. We estimated key nutrients important during pregnancy (folate, calcium, iron, zinc, and iodine) based on average nutrient composition of the food group servings, using the Australian Food, Supplement & Nutrient Database (AUSNUT) 2007 [
A portion size estimation aid (PSEA) included in the tool assisted with portion size quantification. The PSEA contained 80 photographs of a variety of AGTHE foods and drinks displayed in recommended serving sizes. The dietitian analyzing food portions compared the image from the image-based dietary record with images in the PSEA, in order to quantify portion size of the food and drink items in terms of number of AGTHE servings (see
Feedback was provided to participants in week 6 of the study, via a short (1 minute) video designed to relay a simple, visual summary of food group intake compared with AGTHE recommendations. The video was transmitted to the Diet Bytes notebook, through the Evernote app on participants’ smartphones. Participants were sent a text message informing them that their feedback was available to view. The video could be paused and replayed as often as desired. Participants were given a few days to view their feedback and were then contacted later in the week by a dietitian for a telephone consultation. In the telephone conversation, results were discussed in greater detail, including core and energy-dense, nutrient-poor food group results and intakes of selected nutrients, to provide practical tailored examples of foods and serving sizes to optimize the participant’s pregnancy dietary intake.
The Selected Nutrient and Diet Quality (SNaQ) analysis tool and portion size estimation aid (PSEA) for analysis of image-based dietary records in the Diet Bytes and Baby Bumps study. AGTHE:
We entered image-based dietary records into the nutrient composition software FoodWorks Professional version 7.0.3016 (Xyris Software [Australia] Pty Ltd) using the nutrient composition tables AUSNUT 2007 [
We enrolled 27 women in the DBBB study, with a median (interquartile range) age 28.8 (27.5-32.5) years, with 1 participant withdrawing due to time constraints. Of the remaining 26 participants, all were born in Australia, 8 (31%) identified as being of Indigenous descent, and all spoke only English at home. At study enrollment, 4 (15%) participants smoked tobacco products. At enrollment, participants ranged from 6 to 24 weeks’ gestation, with a mean (SD) of 18 (5) weeks. A total of 4 participants were in their first trimester of pregnancy, and 22 in their second trimester. For 15 (58%) participants it was the first pregnancy; 14 (54%) participants had an undergraduate or postgraduate university degree; and 2 developed health conditions (gestational diabetes and anemia) during the study.
Over half (n=17, 65%) had received nutrition advice from a health professional previously, although only 5 (19%) had received advice from a dietitian. Other sources of nutrition advice came from a general practitioner (n=10, 38%), midwife (n=5, 19%), obstetrician (n=1, 4%), or an antenatal clinic (n=1, 4%). Advice received focused on use of multivitamin supplements (n=12, 46%), managing morning sickness (n=7, 27%), healthy eating throughout pregnancy (n=7, 27%), weight gain during pregnancy (n=5, 19%), healthy eating during breastfeeding (n=5, 19%), or breastfeeding (n=4, 15%). Participants had also accessed pregnancy nutrition information from other sources, including friends (n=11, 42%), nongovernment websites (n=11, 42%), family (n=10, 38%), government websites (n=9, 35%), smartphone apps (n=7, 27%), and community groups, including mothers’ groups (n=2, 8%); 3 (12%) participants had not accessed any of these sources of information. A total of 11 (42%) participants felt they had received enough information about healthy eating for themselves and their baby at the time of enrollment, 13 (50%) were unsure, and 2 (8%) said they had not received enough information.
All participants used their smartphones for sending text messages (short message service, SMS) (n=26, 100%), and the majority for receiving SMS (n=25, 96%), searching or browsing the Internet (n=25, 96%), making voice calls (n=24, 92%), taking photos (n=24, 92%), sending or uploading photos (n=24, 92%), using apps (n=22, 85%), and taking notes (n=20, 77%). Over half (n=16, 62%) used their smartphones for taking videos and 12 (46%) to send or upload these videos. The majority of participants (n=18, 69%) had an Apple iPhone, and 8 (31%) had a Google Android phone. Only 4 (15%) had used their smartphones for making voice recordings.
Of the 26 participants, 24 (92%) recorded on all 3 days of the image-based dietary record, 1 participant recorded 2 days, and 1 recorded only 1 day. The participant recording on only 1 day was subsequently excluded from further analyses, and therefore further results are for the 25 participants with dietary records adequate for analysis. We used average food group and micronutrient intakes from participants’ multiple-day image records for this analyses.
Intake of core foods as assessed by the Selected Nutrient and Diet Quality (SNaQ) brief analysis tool from the Diet Bytes and Baby Bumps image-based dietary records (n=25).
Food group | Food group intake in servings/day | AGTHEb recommended intake during pregnancy in servings/day | Meeting recommended intake of servings | |||
Mean (SD) | Median (IQRa) | No. of servings | n (%) | |||
Grains and cereals | 4.8 (2.0) | 4.7 (3.6-6.5) | 8.5 | ≥8.5 | 1 (4) | |
Vegetables | 2.4 (1.4) | 2.2 (1.2-3.5) | 5 | ≥5 | 1 (4) | |
Fruit | 1.9 (1.6) | 1.7 (0.9-2.5) | 2 | ≥2 | 10 (40) | |
Lean meat | 2.0 (1.0) | 1.9 (1.4-2.9) | 3.5 | ≥3.5 | 2 (8) | |
Dairy | 2.1 (1.3) | 1.8 (1.3-2.7) | 2.5 | ≥2.5 | 10 (40) | |
Unsaturated spreads and oils | 1.9 (1.4) | 2.0 (0.5-3.0) | 0-2.5 | 0-2.5 | 16 (64) | |
Energy-dense, nutrient-poor foods | 3.7 (1.9) | 3.5 (2.4-3.9) | 0-2.5 | 0-2.5 | 7 (28) | |
Grains and cereals | 4.7 (2.3) | 4.3 (3.4-6.1) | 8.5 | ≥8.5 | 1 (13) | |
Vegetables | 2.0 (1.4) | 1.6 (1.1-3.2) | 5 | ≥5 | 0 (0) | |
Fruit | 1.4 (1.9) | 0.9 (0.0-2.3) | 2 | ≥2 | 2 (25) | |
Lean meat | 1.6 (0.9) | 1.5 (0.8-2.0) | 3.5 | ≥3.5 | 0 (0) | |
Dairy | 2.5 (1.9) | 2.3 (1.0-3.4) | 2.5 | ≥2.5 | 4 (50) | |
Unsaturated spreads and oils | 0.8 (0.8) | 0.7 (0.8-1.7) | 0-2.5 | 0-2.5 | 8 (100) | |
Energy-dense, nutrient-poor foods | 4.1 (2.9) | 3.7 (1.6-7.1) | 0-2.5 | 0-2.5 | 2 (25) | |
Grains and cereals | 4.9 (1.9) | 4.9 (3.6-6.9) | 8.5 | ≥8.5 | 0 (0) | |
Vegetables | 2.6 (1.4) | 2.4 (1.7-3.5) | 5 | ≥5 | 1 (6) | |
Fruit | 2.2 (1.4) | 1.8 (1.4-2.7) | 2 | ≥2 | 8 (47) | |
Lean meat | 2.2 (1.0) | 2.0 (1.7-3.1) | 3.5 | ≥3.5 | 2 (12) | |
Dairy | 1.9 (0.9) | 1.7 (1.3-2.7) | 2.5 | ≥2.5 | 6 (36) | |
Unsaturated spreads and oils | 2.3 (1.4) | 2.8 (1.0-3.3) | 0-2.5 | 0-2.5 | 8 (47) | |
Energy-dense, nutrient-poor foods | 3.5 (1.3) | 3.5 (2.4-3.9) | 0-2.5 | 0-2.5 | 5 (29) |
aIQR: interquartile range (25th-75th percentiles).
bAGTHE:
Correlation and agreement for energy and selected nutrient intake from mean 3-day image-based dietary records in the Diet Bytes and Baby Bumps study (n=25 participants) analyzed by the Selected Nutrient and Diet Quality (SNaQ) tool and FoodWorks (FW) nutrient analysis software.
Nutrient | Method | Input, median (IQRa) | ρ ( |
n (%) <EARb | n (%) ≥EAR to <RDIc | n (%) ≥RDI | Cohen kappa ( |
|
Energy (kJ/day) | SNaQ | 8418.33 (7755.83-10,004.17) | .898 (<.001) | N/Ad | .031e (.67) | |||
FW | 7738.89 (6329.94-8995.05) | |||||||
Iron (mg/day) | SNaQ | 11.30 (8.93-15.08) | .812 (<.001) | 21(84) | 0 (0) | 4 (16) | .533 (<.001) | |
FW | 13.54 (10.75-21.47) | 19 (76) | 3 (12) | 3 (12) | ||||
Calcium (mg/day) | SNaQ | 877.36 (653.74-1181.60) | .791 (<.001) | 12 (48) | 4 (16) | 9 (36) | .488 (.001) | |
FW | 831.01 (672.39-1000.89) | 13 (52) | 6 (24) | 6 (24) | ||||
Folate, total DFEf (µg/day) | SNaQ | 851.90 (225.15-1156.15) | .893 (<.001) | 11 (44) | 1 (4) | 13 (52) | .559 (.001) | |
FW | 820.20 (393.53-1383.00) | 8 (32) | 2 (8) | 15 (60) | ||||
Iodine (µg/day) | SNaQ | 167.00 (93.52-311.28) | .955 (<.001) | 11 (44) | 4 (16) | 10 (40) | .803 (<.001) | |
FW | 171.42 (92.58-300.20) | 12 (48) | 3 (12) | 10 (40) | ||||
Zinc (mg/day) | SNaQ | 13.09 (10.46-19.56) | .905 (<.001) | 3 (12) | 4 (16) | 18 (72) | .741 (<.001) | |
FW | 14.66 (10.24-21.24) | 3 (12) | 5 (20) | 17 (68) | ||||
Energy (kJ/day) | SNaQ | 8418.33 (7755.83-10,004.17) | .898 (.000) | N/A | .031e (.67) | |||
FW | 7738.89 (6329.94-8995.05) | |||||||
Iron (mg/day) | SNaQ | 9.50 (7.70-10.85) | .510 (.009) | 25 (100) | 0 (0) | 0 (0) | Constants (no statistics computed) | |
FW | 11.78 (8.53, 13.73) | 25 (100) | 0 (0) | 0 (0) | ||||
Calcium (mg/day) | SNaQ | 809.90 (653.75-1181.70) | .888 (<.001) | 14 (56) | 2 (8) | 9 (36) | .554 (<.001) | |
FW | 736.61 (663.19-927.37) | 17 (68) | 3 (12) | 5 (20) | ||||
Folate, total DFE (µg/day) | SNaQ | 319.00 (240.25-433.35) | .600 (.002) | 21 (84) | 4 (16) | 0 (0) | -.068 (.52) | |
FW | 409.79 (259.74-642.22) | 16 (64) | 2 (8) | 7 (28) | ||||
Iodine (µg/day) | SNaQ | 99.00 (79.80-139.05) | .850 (<.001) | 22 (88) | 2 (8) | 1 (4) | .632 (<.001) | |
FW | 104.25 (86.46-130.95) | 22 (88) | 2 (8) | 1 (4) | ||||
Zinc (mg/day) | SNaQ | 10.60 (8.40-13.10) | .745 (<.001) | 7 (28) | 6 (24) | 12 (48) | .572 (<.001) | |
FW | 10.63 (8.89-13.47) | 6 (24) | 9 (36) | 10 (40) |
aIQR: interquartile range (25th-75th percentiles).
bEAR: estimated average requirement. EAR is a nutrient level estimated to meet the requirements of 50% of the healthy individuals in a life stage or gender group, per day (EARs for nutrients as follows: iron 22 mg, calcium 840 mg, folate 520 µg, iodine 160 µg, zinc 9 mg) [
cRDI: recommended dietary intake. RDI is the average dietary intake level sufficient to meet nutrient requirements of 97% to 98% of healthy individuals in a life stage or gender group, per day (RDIs for nutrients as follows: iron 27 mg, calcium 1000 mg, folate 600 µg, iodine 220 µg, zinc 11 mg) [
dN/A: not applicable.
eKappa for energy intake in categories of 1000 kJ.
fDFE: dietary folate equivalents.
Participant’s perceived acceptability for receiving dietary counselling in the Diet Bytes and Baby Bumps study (n=22a) (survey questions with agree-disagree responses).
Questions | Strongly agree |
Agree |
Neutral |
Disagree |
Strongly disagree |
I believe that the combination of the summary of my dietary intake that I received via my mobile/smartphone and the follow-up with the dietitian was helpful. | 12 (55) | 9 (41) | 1 (5) | 0 (0) | 0 (0) |
The summary of my dietary intake that I received via my mobile/smartphone was easy to understand on its own. I did not need to speak to a dietitian to clarify. | 2 (9) | 5 (23) | 6 (27) | 8 (36) | 1 (5) |
The summary of my dietary intake that I received via my mobile/smartphone was difficult to understand. | 0 (0) | 2 (9) | 3 (14) | 13 (59) | 4 (18) |
Neither the summary of the results from the analysis of my photographic dietary record that I received on my mobile/smartphone nor the advice that I received from the dietitian was helpful. | 0 (0) | 1 (5) | 1 (5) | 4 (18) | 16 (73) |
an=22. Two participants did not receive the telephone counselling (1 gave birth before it could be given and 1 did not respond to contact) and 2 participants did not answer this survey.
Participant’s perceived acceptability for receiving dietary counselling in the Diet Bytes and Baby Bumps study (n=22a) (survey questions with yes/no responses).
Questions | Yes, n (%) | No, n (%) |
I have changed my diet as a result of the nutrition advice that I received as part of this study. | 17 (77) | 5 (23) |
I have changed the kinds of foods I eat. | 16 (73) | 6 (27) |
I have changed the amount of food I eat. | 8 (36) | 14 (64) |
I have changed the cooking methods I use. | 3 (14) | 19 (86) |
I have changed how I keep track of what I eat and drink. | 5 (23) | 17 (77) |
I have made other changes. | 1 (5) | 21 (95) |
an=22. Two participants did not receive the telephone counselling (1 gave birth before it could be given and 1 did not respond to contact) and 2 participants did not answer this survey.
Some participants thought the advice from a dietitian was useful and helped to clarify the feedback provided via the video summary; for example:
...the phone consult was very useful to me. Without this the written feedback would have been far less meaningful. I did like the visual graphs to help me understand the information.
Additionally, another participant commented:
It was very detailed and thorough and easier to understand what should be done to improve my diet compared to the diet summary received on Evernote.
Others reported not making changes as a result of the feedback, due to already meeting requirements or not being able to fit all the recommended servings into their daily intake.
Some participants felt that DBBB could be improved by keeping the image-based records for a longer duration and by taking notes, rather than images, for certain foods such as snacks and water. More SMS reminders were requested, as some participants reported forgetting to take images. One commented that having to take an image before eating when you were hungry was inconvenient:
It’s inconvenient to take pictures of food before eating when hungry (which is most of the time), however I think this is a useful way of assessing dietary intake.
The majority of respondents (n=18, 82%), preferred receiving nutrition feedback via the combination of the video summary and follow-up telephone consultation with a dietitian. One indicated that she preferred the video feedback alone, and 3 preferred the consultation with the dietitian alone. Only 1 participant indicated an alternative method for receiving nutrition advice, via a printable email summary.
We observed strong positive correlations between the SNaQ tool and the nutrient analysis software for estimates of total energy intake and all selected micronutrients (iron, calcium, zinc, folate, and iodine), both with and without micronutrient supplements included in the analysis. However, SNaQ overestimated energy intake compared with the FoodWorks analysis (8418 kJ vs 7739 kJ) and underestimated intakes of some micronutrients (iron, iodine, and zinc when supplements were included in the analysis; iron, folate, and iodine when supplements were excluded). The relatively minor differences in intakes were not clinically important differences, as evidenced by the comparison of classifications of nutrient intake adequacy (EAR, RDI) using Cohen kappa (in
Specifically designed as a brief tool, the SNaQ tool therefore did not include all foods within the food composition database, and as such may have underestimated some micronutrients. When we removed supplements from the analysis, the SNaQ tool did not show significant agreement with the nutrient software analysis for folate (kappa=-.068,
The majority of pregnant women in the DBBB study did not meet the recommended AGTHE target for daily servings of grain and cereal foods, vegetables, fruit, meat, and dairy. The median daily servings of unsaturated spreads and oils met recommendations, while median intakes of energy-dense, nutrient-poor foods exceeded recommendations, with less than a third of participants consuming within the target 0-2.5 servings/day. When we evaluated food intakes excluding micronutrient supplements, both the SNaQ and nutrient composition software showed that median intakes of selected key micronutrients important in pregnancy were lower than the EAR for iron, calcium, folate, and iodine. When we included vitamin and mineral supplements use, the median intake of iron was still below the EAR.
Intakes of energy-dense, nutrient-poor foods were high, with the majority (n=18, 72%) exceeding the maximum target of 2.5 servings/day. In other cohorts of pregnant Australian women, it has been reported that meeting AGTHE and NRV targets is challenging [
Prior to being in the study, less than half (n=12, 46%) of participants had received information on prenatal nutrient supplements (including folic acid and iron) during their pregnancy, although the results of this study imply that micronutrient supplementation use may help women meet pregnancy EARs, particularly for iron, folate, and iodine. Only approximately a quarter of participants (n=7, 27%) had received advice on healthy eating during pregnancy prior to the study. Nutrition knowledge among pregnant women in Australia is suboptimal, with one cross-sectional study of 400 pregnant women showing that over half (65%) of participants were not familiar with AGTHE recommendations [
The majority (n=17, 77%) of participants who completed the final survey reported that they had made changes to their dietary intake as a result of receiving the personalized feedback, which consisted of the video summary and the telephone consultation with the dietitian. The preferred method of receiving dietary advice was from the video summary and the dietitian consultation combined, with 95% (n=21) of participants agreeing that this combined way of receiving feedback was helpful. Previous research in the area of apps for dietary feedback during pregnancy supports our findings in this study. A recent evaluation of a Dutch online coaching program delivered by a mobile health platform (called Smarter Pregnancy) resulted in improvements in vegetable, fruit, and folic acid intake in pregnant women, although these were not statistically significant, and high compliance with positive feedback from participants was reported [
Limitations of our study include the small sample size of 25 participants completing the study protocol. A review [
Participants in the DBBB study may not be representative of all Australian women. Those without smartphones were excluded from participating, and therefore these results may not be representative of women who are economically vulnerable or who have other reasons for not owning a smartphone. We did not collect data on prepregnancy weight and weight gain during pregnancy, and therefore we do not know whether study participants were achieving recommendations for appropriate pregnancy weight gain. The median age of DBBB study participants (28.8 years) was slightly lower than the NSW state median age of women giving birth in 2014 (31.2 years) [
To our knowledge, this study is the first to evaluate the use of image-based dietary records for dietary assessment in pregnant women, including Indigenous Australian women, and demonstrated that the SNaQ tool can adequately assess key nutrient intakes during pregnancy. With training and practice, the SNaQ tool has the potential to be both time and resource saving as a dietary assessment tool for dietitians, while reducing the burden of recording associated with traditional methods for participants. Importantly this study highlights that using an image-based dietary record in combination with individual phone consultation with a dietitian for the provision of dietary feedback during pregnancy is acceptable. The Diet Bytes method for nutrition assessment and provision of personally tailored feedback may be a useful method for dietitians to assist women in optimizing their food and nutrient intakes during pregnancy.
Australian Guide to Healthy Eating
Australian Longitudinal Study on Women’s Health
Australian Food, Supplement & Nutrient Database
Diet Bytes and Baby Bumps
estimated average requirement
Nutrient Reference Value
New South Wales
portion size estimation aid
recommended dietary intake
short message service
Selected Nutrient and Diet Quality
MER designed the study protocol and materials. MER and AMA were responsible for participant recruitment, data collection, dietary analysis, and provision of nutrition feedback to participants. AMA analyzed results and prepared this manuscript and MER, CEC, KMR, and LJB assisted with its development and revision. All authors significantly contributed to this research and have read and approved the final manuscript. AMA undertook this research as a partial requirement for the degree of PhD (Nutrition and Dietetics) with the University of Newcastle and is supported by an International Postgraduate Award Scholarship and National Health and Medical Research Council of Australia Project Grant (APP1002733). CEC is supported by a National Health and Medical Research Council of Australia Senior Research Fellowship. Components of this study were supported by a University of Newcastle New Staff Grant awarded to MER. The authors would like to acknowledge and thank Loretta Weatherall for her assistance with recruitment for this study, Katherine Brain for her research assistance, and the women who participated in the Diet Bytes and Baby Bumps study.
None declared.