maternal diet during early childhood, but not pregnancy, predicts diet quality and fruit and...

Maternal diet during early childhood, but notpregnancy, predicts diet quality and fruit and vegetableacceptance in offspring

Amy M. Ashman*†, Clare E. Collins‡, Alexis J. Hure§, Megan Jensen¶ andChristopher Oldmeadow**,††

*School of Health Sciences, The University of Newcastle, Callaghan, New South Wales, 2308, Australia, †Gomeroi gaaynggal centre, The University ofNewcastle, Tamworth, New South Wales 2340, Australia, ‡Priority Research Centre in Physical Activity and Nutrition, The University of Newcastle,Callaghan, New South Wales 2308, Australia, §School of Medicine and Public Health, The University of Newcastle, Callaghan, New South Wales 2308,Australia, ¶CHU Sainte-Justine, Montréal, Quebec H3T 1C5, Canada, **Clinical Research Design, IT and Statistical Services, Hunter Medical ResearchInstitute, Rankin Park, New South Wales 2305, Australia, and ††School of Medicine and Public Health, Faculty of Health and Medicine, The University ofNewcastle, Callaghan, New South Wales 2308, Australia

Abstract

Studies have identified prenatal flavour exposure as a determinant of taste preferences in infants; however, thesestudies have focused on relatively small samples and limited flavours. As many parents struggle with gettingchildren to accept a variety of nutritious foods, a study of the factors influencing food acceptance is warranted.The objective of this study was to determine whether exposure to a wider variety of fruit and vegetables andoverall higher diet quality in utero results in acceptance of a greater variety of these foods and better diet qualityfor offspring during childhood. This study is a secondary data analysis of pregnant women (n = 52) and theirresulting offspring recruited for the Women and Their Children’s Health study in NSW, Australia. Dietaryintake of mothers and children was measured using food frequency questionnaires. Diet quality and vegetableand fruit variety were calculated using the Australian Recommended Food Score and the Australian Child andAdolescent Recommended Food Score. Associations between maternal and child diet quality and variety wereassessed using Pearson’s correlations and the total effect of in utero maternal pregnancy diet on childhood dietwas decomposed into direct and indirect effect using mediation analysis. Maternal pregnancy and post-natal dietwere both correlated with child diet for overall diet quality and fruit and vegetable variety (P < 0.001). Mediationanalyses showed that the indirect effect of maternal pregnancy diet on child diet was mediated through maternalpost-natal diet, particularly for fruit (P = 0.045) and vegetables (P = 0.055). Nutrition intervention should there-fore be aimed at improving diet quality and variety in mothers with young children, in order to subsequentlyimprove eating habits of offspring.

Keywords: pregnancy, child, diet quality, variety, fruit, vegetable.

Correspondence: Professor Clare E. Collins, Priority Research Centre in Physical Activity and Nutrition, The University of Newcastle,Callaghan, NSW 2308, Australia. E-mail: [email protected]

Introduction

Vegetables and fruit are cornerstones of a healthfuldiet, and their consumption has long been linked withchronic disease prevention and positive health out-comes (National Health and Medical ResearchCouncil, Department of Health and Ageing 2013).However, the 2007 Children’s Nutrition and PhysicalActivity Survey showed that three quarters of Aus-

tralian children aged 2 to 8 years did not meet veg-etable recommendations for a healthy, nutrient-densevaried diet, with inadequate fruit and vegetableintake, and high saturated fat and sugar intakehighlighted (Department of Health and Ageing,Department of Agriculture, Fisheries and Forestry2008). Poor eating habits in childhood have beenshown to track into adulthood and can contribute tothe development of chronic disease later in life

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DOI: 10.1111/mcn.12151

Original Article

1© 2014 John Wiley & Sons Ltd Maternal and Child Nutrition (2014), ••, pp. ••–••

mailto:[email protected]

(Berenson et al. 1992; Mikkilä et al. 2005; Northstone& Emmett 2008). It is therefore imperative to estab-lish healthy eating habits in early childhood.

In westernized countries, most of the diseaseburden from poor nutrition is due to excess intake ofenergy-dense, nutrient-poor foods, which are foodshigh in energy, saturated fat and added or refinedsugars and/or salt (National Health and MedicalResearch Council, Department of Health and Ageing2013). These energy-dense, nutrient-poor foods arereadily available and affordable (Beauchamp &Mennella 2009). Many parents report difficulties withfeeding children a wide variety of nutrient-densefoods from the core food groups (vegetables andlegumes, fruit, whole grains, lean meat and vegetarianalternatives and dairy foods), particularly for vegeta-bles (National Health and Medical Research Council,Department of Health and Ageing 2013). Encourag-ing vegetable intake from an early age remains achallenging area for both parents and dietetic profes-sionals (Carruth et al. 2004; Maier et al. 2007).If children enjoy the flavour of a food, they are morelikely to consume it (Benton 2004; Beauchamp &Mennella 2009). Young children are also more likelyto accept a food if its flavour is familiar to them(Beauchamp & Mennella 2009). An exploration intofood acceptance in young children can therefore leadto valuable insight upon which to base nutritionintervention.

The majority of observational studies of fruit andvegetable intake in children have focused on post-natal influences on food choices (Benton 2004; Savageet al. 2007; Fisk et al. 2011). Experimental studiesaiming to increase fruit and vegetable intake in youngchildren have also focused primarily on post-natalexposure (Birch et al. 1998; Forestell & Mennella2007; Maier et al. 2007; Mennella et al. 2008). Thesestudies have shown that exposure to a food throughbreast milk or formula or exposure to a solid food

during infancy promotes long-lasting effects, namelyfamiliarity of and preference for this food (Schaalet al. 2000; Forestell & Mennella 2007; Mennella et al.2008).

While extensive studies examining post-natal influ-ences on child taste preferences are available, limiteddata are available on the prenatal influence on tastedevelopment. It has been suggested that infants areborn with innate preferences for sweet, salty andumami foods over bitter or sour foods (Beauchamp &Mennella 2009). However, humans can override theseinnate preferences and develop preferences for bitteror sour foods, including certain vegetables and fruit(Maier et al. 2007; Mennella et al. 2008; Beauchamp &Mennella 2009). Classic studies by Mennella and col-leagues have increased our understanding of theinfluence of maternal diet and in utero flavour expo-sure on the future taste preferences of offspring(Mennella et al. 2001). By 13–15 weeks gestation, thefetus can perceive tastes and smells while still in thewomb via amniotic fluid (Mennella et al. 1995, 2001),suggesting the earliest taste and smell experiencesbegin during gestation (Mennella et al. 2001). Indeed,Mennella has shown that prenatal exposure to certainflavours is associated with a greater acceptance ofthese foods in infancy (Mennella et al. 2001). In arandomised controlled trial, mothers in the experi-mental group drank 300 mL carrot juice 4 days perweek for three consecutive weeks during the last tri-mester of pregnancy. At 5–6 months old, the infantswho had been exposed to the carrot flavour prenatallyexhibited less negative facial responses while beingfed carrot-flavoured cereal, relative to plain cereal(P = 0.01). The control group exhibited the oppositetendency, although this was not significant. (Mennellaet al. 2001). Multiple animal studies on rat, pig, sheepand rabbit young support the finding that prenatalflavour learning influences feeding preferences(Bilkó et al. 1994; Bayol et al. 2007; Simitzis et al. 2008;

Key messages

• Maternal post-natal diet, not pregnancy diet, is associated with child diet.• Maternal post-natal diet, not maternal pregnancy diet, predicted child diet quality, and vegetable and fruit

variety.• Nutrition interventions should aim to improve diet quality and variety in mothers with young children.

A.M. Ashman et al.2

© 2014 John Wiley & Sons Ltd Maternal and Child Nutrition (2014), ••, pp. ••–••

Oostindjer et al. 2009). Although studies in humanshave been of relatively small sample sizes and usedonly one or two foods, these early studies and animalmodels support the hypothesis for in utero flavourlearning (Schaal et al. 2000; Mennella et al. 2001).

Whole foods contain not only macro- andmicronutrients but a range of other non-nutrientcomponents, including phytochemicals that offerprotective effects against disease (National Healthand Medical Research Council, Department ofHealth and Ageing 2013). Therefore, exposure to abroader variety of flavours is associated with greatervariety of food and therefore a broader range ofmacronutrients, micronutrients and non-nutrientcompounds consumed (National Health and MedicalResearch Council, Department of Health and Ageing2013). Diet quality refers to both diet variety andnutritional adequacy, and high-quality diet is associ-ated with improved health outcomes and reducedrisk of chronic disease (National Health and MedicalResearch Council, Department of Health and Ageing2013). Therefore, willingness to accept a wide rangeof flavours will likely increase both diet variety anddiet quality (Mennella et al. 2008). While food prefer-ence refers to foods that are found to be enjoyableand pleasant, food acceptance is also used in thisstudy to refer to those foods which are willinglyconsumed.

There is a need to explore the relationship betweenmaternal diet quality during pregnancy and the dietquality of their offspring. The primary aim of thisstudy was to test whether maternal diet during preg-nancy was associated with childhood diet quality atage 2–3 years. The secondary aim was to test whethermaternal fruit and vegetable intake during pregnancywas associated with child fruit and vegetable con-sumption at 2–3 years. Maternal post-natal diet wasalso considered as a potential predictor of the child’sdiet.

Materials and methods

Study design

This study is a secondary data analysis of the Womenand Their Children’s Health (WATCH) prospective

cohort study, which followed pregnant women andtheir offspring up to 4 years of age (Hure et al. 2012).All pregnant women less than 18 weeks gestationwere eligible to participate in the WATCH study onthe provision that they lived in the local or neighbour-ing areas and were able to commute to John HunterHospital (JHH) in Newcastle, New South Wales, Aus-tralia, for data collection. Participants were recruitedthrough midwives at the JHH antenatal clinic, localmedia coverage and word of mouth. Pregnant womenattended JHH for data collection at 19, 24, 30 and 36weeks gestation.Additionally, data collection for bothmothers and their offspring occurred at post-natalquarterly intervals for the first 12 months, and thenannually until 2–3 years of age (Hure et al. 2012).Between June 2006 and December 2007, 180 womenwere deemed eligible to participate. Of this sample74% remained 2 years after study commencement(Hure et al. 2012). Detailed methods of the WATCHstudy are published elsewhere (Hure et al. 2012). Thiscurrent study uses the dietary intake data frommothers and their offspring up to 3 years of age.

Ethics approval

The WATCH study was approved by the Hunter NewEngland Human Research Ethics Committee in 2006and was also registered with the University of New-castle Human Research Ethics Committee (Hureet al. 2012).All participants in the WATCH study gaveinformed, written consent to participate. Participantsdid not receive any financial incentives to participate.Data were de-identified before commencement ofthis study (Hure et al. 2012).

Data collection

Data were obtained on a number of health,anthropometric, socio-economic and lifestyle vari-ables (Hure et al. 2012). Anthropometric datawere collected by an Accredited Practicing Dietitianand Level 1 Anthropometrist accredited by theInternational Society for the Advancement ofKinanthropometry. Data collection included height/length, weight, circumferences and skinfold thick-nesses of both mothers and their children.

Maternal diet and diet quality in offspring 3


Standardized methods of data collection were used,and are described in detail elsewhere (Hure et al.2012).The following variables were selected for use inthe current analysis.

Data on education, income and marital statuswere self-reported and the questions were modelledon Women’s Health Australia surveys (Brown &Dobson 2000). Further information relating tomedical and socio-economic data were obtainedfrom the ‘Obstetrix’ database, the major record ofantenatal information, birth outcomes and patientand family history in New South Wales, Australia(LeMay 2005).

Dietary intake

Maternal dietary intake was assessed using thevalidated Dietary Questionnaire for EpidemiologyStudies (DQES) food frequency questionnaire(FFQ; Cancer Council Victoria 2005) developedby the Cancer Council of Victoria (Hodge et al.2002). The DQES asks about intake of 74 foods;participants were asked to report intake frequencyover the last 3–12 months, on a 10-point scale of‘never’ to ‘three or more times per day’ (Hure et al.2009). Questions regarding total intake of fruit andvegetables were used to adjust for potential over-reporting of intakes of individual fruit and vegeta-bles (Hure et al. 2009). Nutrient intakes werecomputed from NUTTAB 1995 (Cancer CouncilVictoria 2005).

Child dietary intake was assessed from 2 years ofage using the toddler version of the Australian Childand Adolescent Eating Survey (ACAES; Watson et al.2009; Collins et al. 2013). Mothers completed theACAES for their child, reporting the consumptionfrequency (ranging from ‘never’ to ‘four times perday’) for a range of foods over the preceding 6months. The ACAES is a validated FFQ, which dem-onstrates acceptable reliability for ranking nutrientintakes in Australian toddlers 2–4 years old (Collinset al. 2013). Nineteen questions relate to the intake ofvegetables and 11 items to fruit, with a separatesection to adjust for seasonal fruit intake (Watsonet al. 2009).

Australian Recommended Food Score

The Australian Recommended Food Score (ARFS)and the Australian Child and Adolescent Recom-mended Food Score (ACARFS) were used to calcu-late a diet variety score for mothers and theiroffspring (Collins et al. 2008; Marshall et al. 2012).TheARFS is a numerical value of diet quality and variety(Collins et al. 2008). It is calculated based on the levelof alignment between reported consumption fre-quency of foods featured in the DQES/ACAES andDietary Guidelines for Australian Adults and Austral-ian Guide to Healthy Eating recommendations(Kant & Thompson 1992; The Children’s HealthDevelopment Foundation, Deakin University 1998;National Health and Medical Research Council2003). A higher score reflects greater adherence tonational guidelines and therefore greater diet qualityand variety. Alcohol was not included for this cohort.Respective maximum scores for the ARFS and theACARFS were 72 and 73 (Hure et al. 2009). Thescoring method for the ARFS and ACARFS aredescribed elsewhere (Collins et al. 2008, 2013). Dietvariety scores (sub-scales) were calculated for con-sumption frequency of foods within the core foodgroups, including fruit and vegetables (Collins et al.2008, 2013).

Statistical analysis

Participants were included if dietary data were avail-able for at least one time point. Exploratory analysisfound early and late pregnancy diets were stronglycorrelated. Therefore, the average of both time pointswas taken. The average ACARFS score was calcu-lated for the child diet between ages 2 and 3 years.Pearson’s correlation coefficients were used to assessthe relationship between offspring ACARFS scores(total and subscales) and both maternal pregnancyand maternal post-natal ARFS scores (total andsubscales).The total effect of in utero maternal diet onchildhood diet was decomposed into a direct effectand an indirect effect through maternal post-nataldiet using mediation analysis with standard errors ofparameter estimates estimated using the bootstrapwith 1000 bootstrap replications (MacKinnon &

A.M. Ashman et al.4


Dwyer 1993). Mediation analyses were performedunadjusted for confounders, and also adjusted foreducation, parity (first child or not), maternal age(years) and breastfeeding duration (weeks). Allanalyses were programmed using Stata v13(StataCorp, College Station, TX, USA). We note thatmulti-collinearity is an inherent problem in mediationanalysis, particularly when strong correlation existsbetween the independent and mediating variablesresulting in inflated standard error of mediatingeffects and therefore diminishing the power to detectthem.

Results

Of the 180 women recruited, dietary intake dataduring pregnancy and offspring dietary data at age2–3 years was available for 52 mother–child dyads.Only seven mother–child dyads completed FFQsat all time points: maternal intake in early pregnancy(19 weeks gestation), late pregnancy (36 weeks gesta-tion) and at 2 and 3 years post-partum; child intakeat ages 2 and 3 years. Characteristics of the 52 mothersincluded are summarized in Table 1. Mothers wereaged between 19 and 41 years (mean age 30.2 ± 5.4),and for the majority of mothers this pregnancywas their first (n = 26) or second child (n = 15).

Around half of the women had a pre-pregnancybody mass index (BMI) in the normal weight rangeof 18.5–24.9 kg m−2 (52.9%), 25.5% were over-weight (BMI 25–29.9 kg m−2) and 19.6% were obese(BMI > 30 kg m−2). One participant was underweight(BMI < 18.5 kg m−2), two women had a BMI of35–39.9 kg m−2 (obese class 2) and one had a BMI ofgreater than 40 kg m−2 (obese class 3). Four partici-pants had smoked at some point during their preg-nancy. Of the 52 mothers in this analysis, 75% had ayear 12 high school certificate or higher educationalattainment, compared with 71.3% (n = 160) from theWATCH population cohort (Hure et al. 2011).

Table 2 reports mean ARFS and ACARFS scoresof mothers and children at each time point. TotalARFS and fruit and vegetable sub-scores were verysimilar at both pregnancy time points. Mean preg-nancy ARFS (average early and late) was 31 out of amaximum 72. Twelve participants obtained scores≥40, and six obtained scores that were less than 20.Mean fruit score was 6.2 (maximum 14), and meanvegetable score was 12.2 (maximum 22). For children,mean ACARFS (total and sub-scales) were verysimilar at ages 2 and 3. The mean total ACARFS intoddlerhood was 31.2 (maximum 73). Nine partici-pants obtained scores of ≥40, and four participantsobtained scores of ≤20. Mean fruit score was 6.0

Table 1. Demographic characteristics of women (n = 52) participating in the WATCH cohort study

Characteristic

Min-max values(n = 52)

Sample (n = 52) WATCH cohort(n = 179)

Age (years), mean ± SD 19.4–41.2 30.2 ± 5.4 28.7 ± 5.7Pre-pregnancy weight (kg), median [IQR] 48–140 67.5 [18.5] 65.0 [21.0]Height (cm), median [IQR] 156.3–182.6 165.7 [6.0] 164.2 [9.1]Pre-pregnancy BMI (kg m−2), median [IQR] 17.4–48.1 24.4 [6.1] 24.4 [7.8]Length of gestation (weeks), median [IQR] 32–42 39.4 [2.0] 39.4 [2.0]Nulliparous, n (%) 26 (50%) 77 (43%)

Educational attainment N %

Less than year 12 or equivalent 13 25%Year 12, certificate, trade or apprenticeship 23 44%University degree 16 31%

BMI, body mass index; IQR, interquartile range; SD, standard deviation.



(maximum 12), and mean vegetable score was 9.6(maximum 21). Maternal diet between early and latepregnancy was found to be significantly correlated forARFS total and fruit and vegetable sub-scores(P < 0.0001). Therefore, the average of early and latepregnancy was used to show the association betweenmaternal pregnancy and post-natal diets and theoffspring’s diet at age 2 to 3 years (Table 3). Maternalpregnancy diet was found to be strongly correlatedwith maternal diet for both total ARFS (r = 0.85,P < 0.001) and vegetable sub-score (r = 0.82,P < 0.001) and moderately correlated for the fruitsub-score (r = 0.58, P < 0.001). Moderate correlationswere found between maternal pregnancy diet andchild diet (average of 2–3 years) for ARFS (r = 0.66,P < 0.001), fruit (r = 0.46, P < 0.001) and vegetables

(r = 0.52, P < 0.001). Moderate correlations were alsofound between maternal post-natal diet and child diet(average of 2–3 years) for total ARFS (r = 0.65,P < 0.001), fruit (r = 0.59, P < 0.001) and vegetable(r = 0.61, P < 0.001) sub-scores (Table 3).

Mediation analysis was performed to determine thedirect effect of maternal pregnancy diet on child dietat age 2–3 years and the effect indirectly mediatedthrough maternal post-natal diet (Fig. 1). Mediationresults are presented unadjusted for confounders andadjusted for education, parity, maternal age andbreastfeeding duration in Table 4. After adjusting forconfounders, a statistically significant total effect of in

utero diet on child diet (coefficient = 0.64, P < 0.0001)was found for total diet quality (ARFS). However,neither direct nor indirect effects were statistically

Table 2. Mean (SD) of ARFS and ACARFS (total and sub-scores) for women and their children participating in the WATCH cohort study, overtime

Time point Diet Quality Score (maximum score) Maternalmean (SD)

Childmean (SD)

Pregnancy (average of early and late pregnancy) ARFS total (72) 31.0 (9.7) NAARFS fruit score (14) 6.2 (2.6) NAARFS vegetable score (22) 12.2 (5.0) NA

Post-natal: maternal and child (child age 2 years) ARFS/ACARFS total (72/73) 31.7 (8.7) 30.7 (9.5)ARFS/ACARFS fruit score (14/12) 5.2 (2.9) 5.6 (2.6)ARFS/ACARFS vegetable score (22/21) 13.7 (4.3) 9.3 (4.8)

Post-natal: maternal and child (child age 3 years) ARFS/ACARFS total (72/73) 28.6 (11.2) 31.1 (10.3)ARFS/ACARFS fruit score (14/12) 5.6 (3.7) 6.5 (3.0)ARFS/ACARFS vegetable score (22/21) 11.7 (5.3) 9.8 (4.7)

Average of post-natal maternal and child(child age 2 and 3 years)

ARFS/ACARFS total (72/73) 30.3 (9.5) 31.2 (9.7)ARFS/ACARFS fruit score (14/12) 5.04 (3.2) 6.00 (2.8)ARFS/ACARFS vegetable score (22/21) 12.9 (4.7) 9.6 (4.7)

ACARFS, Australian Child and Adolescent Recommended Food Score; ARFS, Australian Recommended Food Score; NA, not applicable; SD,standard deviation.

Table 3. Pearson’s correlations between maternal pregnancy diet, maternal post-natal diet and child diet at 2–3 years (n = 52 pairs)

Maternal pregnancyand post-natal diets

Maternal post-nataland child diets

Maternal pregnancyand child diets

Correlation P-value Correlation P-value Correlation P-value

Diet quality* 0.85 <0.001 0.65 <0.001 0.66 <0.001Fruits 0.58 <0.001 0.59 <0.001 0.46 <0.001Vegetables 0.82 <0.001 0.61 <0.001 0.52 <0.001

*Maternal diet quality measured using the Australian Recommended Food Score (ARFS) and child diet quality measured using the AustralianChild and Adolescent Recommended Food Score (ACARFS), with fruits and vegetables sub-scales from each tool.

A.M. Ashman et al.6


significant on their own (direct effect = 0.45,P = 0.125; indirect effect = 0.18, P = 0.419). For fruitvariety, a statistically significant total effect of in utero

diet on child diet (coefficient = 0.46, P = 0.014) wasfound, and this was comprised mainly (∼70%) of anindirect effect mediated through maternal post-nataldiet (coefficient = 0.32, P = 0.045). For vegetablevariety, the statistically significant total effect of in

utero diet on child diet (coefficient = 0.39, P = 0.03)was almost entirely comprised of an indirect effectmediated through maternal post-natal diet (coeffi-cient = 0.38, P = 0.055, borderline significant).

Discussion

The purpose of this study was to establish whethermaternal diet during pregnancy and after birth isa predictor of toddler diet quality or variety of fruitand vegetable intakes. This study found that muchof the effects of maternal pregnancy diet on child dietare mediated indirectly through maternal post-nataldiet, particularly for fruit and vegetable variety. Thissupports the role of the mother’s current diet qualityin influencing the diet quality of their child at age 2and 3 years.Although it has been suggested that expo-sure in utero to a wide variety of foods may develop abroader taste acceptance in offspring and thereforesupport greater acceptance of a variety of foods inearly childhood (Mennella et al. 2001), the currentstudy did not support a direct effect between mater-nal diet quality during pregnancy and diet quality ofthe offspring. Although initial correlations showedstrong associations between pregnancy and child diet,these relationships were found to be mediated by the

indirect effect of maternal post-natal diet. For youngwomen in the WATCH study, there was no significantdifference in maternal diet quality between preg-nancy and post-partum, and this was previouslyshown in a cross-sectional analysis of young Austral-ian women (Hure et al. 2009).

The results of the current study also highlight thepoor diet quality in this group of women during andafter pregnancy. The mean ARFS scores for total dietquality and fruit and vegetable diet quality indicatethat the consumption patterns of this group ofwomen do not meet the national dietary guidelinesfor health maintenance and chronic disease preven-tion (National Health and Medical Research Council2003). Indeed, many young Australian women arenot meeting national recommendations for healthy,varied, nutrient-dense diets as laid out in the dietaryguidelines and nutrient reference values, regardlessof pregnancy status (Hure et al. 2009). This is of par-ticular concern during pregnancy, given that mater-nal nutrition during pregnancy is linked to long-termmetabolic and endocrine outcomes for the offspring,and poor maternal diet quality is associated withan increased risk of neural tube defects and lowerbirth weight (Kuzawa 2005; Hure et al. 2009; Wadhwaet al. 2009). Although it is suggested that the dietquality scores are arbitrary in terms of definingscores as ‘high’ or ‘low’, the ARFS is useful forranking diet quality and variety at the populationlevel (Hure et al. 2009). Hure et al. found that in acohort of young women, those in the highest ARFSquintile (mean ARFS 42.6) still did not meetnational recommendations for nutrient intake,regardless of pregnancy status (Hure et al. 2009). Asimilar finding was reported for a cohort of mid-agedwomen, with participants in the highest ARFSquintile (mean ARFS: 45.9) still failing to meet rec-ommended intakes for many nutrients (Collins et al.2008). Therefore, it is suggested that a mean ARFSdiet quality score greater than 40 needs to be tar-geted in order to meet national recommendationsfor nutrient intake. In a cohort of pre-schoolers,the median score for the Australian RecommendedFood Score for Pre-Schoolers (ARFS-P) was 36(maximum score 55, minimum score 12). TheARFS-P was closely modelled on the ARFS and it is

Maternalpregnancy diet

Child diet at2–3 years

Maternalpost-natal diet

AB

C

Fig. 1. Mediation model of maternal diet with potential direct (coef-ficient C) and indirect effects (product of path coefficients A and B) onchild diet.



suggested that a score of ≥42 indicated good dietquality (Collins et al. 2014).

Maternal post-natal diet was found to mediate astrong effect on child fruit and vegetable acceptance.These findings mirror an increasing body of researchthat suggests familial environment, particularlymaternal diet, is associated with child dietary intake(van der Horst et al. 2007; Pearson et al. 2009; Fisket al. 2011). Given these findings, it is therefore notsurprising that child ACARFS (total and sub-scores)were suboptimal, reflecting those of mothers. This hassignificant implications for both further research andhealth promotion. The literature shows children arenot consuming enough fruit and vegetables in theirdiets and getting children to eat vegetables is a par-ticular challenge for many parents (Carruth et al.2004; Booth et al. 2006; Maier et al. 2007; Departmentof Health and Ageing, Department of Agriculture,Fisheries and Forestry 2008). Multiple studies haveshown that healthy habits in childhood track into laterlife, so the development of healthy eating habits earlyin life is vital (Berenson et al. 1992; Kelder et al. 1994;Mikkilä et al. 2005; Northstone & Emmett 2008).Many factors beyond the scope of the current studyinfluence child acceptance of foods. In addition toinnate taste preferences and research by Mennella

supporting exposure to taste in utero and via breastmilk, there is evidence for the role of genetics in tastepreference (Fildes et al. 2014). In a study of twins(mean age 3.5 ± 0.27 years), Fildes et al. postulatedthat, while both genetics and environment had signifi-cant impact on food preferences, genetic effects weregreater for vegetables and fruit, and environmentaleffects were greater for energy-dense snacks andstarchy foods (Fildes et al. 2014). This result posesinteresting implications for nutrition interventions.Wardle & Cooke (2010) argue that an individual’sexperience with food can override this innate orgenetic predisposition. Animal and human studiessuggest that food preferences can be socially trans-mitted through food behaviours modelled in thehome or by peers, and that feeding behaviours such asrewards and positive attention for healthy eating tendto increase acceptance (Wardle & Cooke 2010).Repeated exposure through ingestion of unfamiliarfoods also leads to increased acceptance of that food(Cooke 2007). Conversely, coercive feeding practicesor parents showing negative emotions at meal timescan impair child enjoyment of eating (Wardle &Cooke 2010). Repeated exposure including sociallearning through parental, familial and peer foodmodelling and positive reinforcement of healthy

Table 4. Mediation analysis quantifying direct and indirect effects of maternal diet during pregnancy on child diet at 2–3 years (n = 52 pairs)*

Unadjusted Adjusted†

Coefficient 95% CI P-value Coefficient 95% CI P-value

Diet quality‡

Direct 0.55 0.02, 1.07 0.040 0.45 −0.13, 1.03 0.125Indirect 0.20 −0.24, 0.63 0.379 0.18 −0.26, 0.62 0.419Total 0.75 0.47, 1.02 <0.001 0.64 0.28, 0.99 <0.001

FruitsDirect 0.23 −0.10, 0.57 0.170 0.14 −0.28, 0.56 0.515Indirect 0.31 0.06, 0.55 0.013 0.32 0.01, 0.64 0.045Total 0.54 0.26, 0.82 <0.001 0.46 0.10, 0.83 0.014

VegetablesDirect 0.09 −0.36, 0.53 0.704 0.01 −0.50, 0.52 0.974Indirect 0.46 0.08, 0.85 0.018 0.38 −0.01, 0.77 0.055Total 0.55 0.28, 0.83 <0.001 0.39 0.04, 0.74 0.030

*The direct effect is the coefficient of maternal pregnancy diet on child diet and the indirect effect is the product of the path coefficients formaternal pregnancy diet on maternal post-natal diet and maternal post-natal diet on child diet. †Adjusted for education (ordinal), parity (firstchild or not), maternal age (years) and duration of any breastfeeding (weeks). ‡Maternal diet quality measured using the Australian Recom-mended Food Score (ARFS) and child diet quality measured using the Australian Child and Adolescent Recommended Food Score (ACARFS),with fruits and vegetables sub-scales from each tool.

A.M. Ashman et al.8


eating are therefore all valuable strategies that assistwith development of healthy food preferences inchildren.

It is an important finding of the current study thatchildren appear to be influenced by their mothersintake at such an early age, as it supports the literatureon social learning that mothers can act as role modelsfor their children by consuming a wide variety ofnutritious foods, including fruit and vegetables, inorder to increase the likelihood that their childrenwill accept the same (Wardle & Cooke 2010). Thishighlights opportunities for randomised controlledtrials directed at improving mothers’ diet quality inorder to positively impact child diet quality.

This study had a number of limitations. Given thiswas a secondary data analysis, and diet quality wasnot the primary outcome, the study may have beenunderpowered to detect some associations betweenmother and child diet quality. As data collection tookplace over several years, not all participants attendedevery data collection session, and therefore, it is alimitation of this study that only 52 women of therecruited 180 women could be included in this analy-sis. We attribute the low retention rate for the FFQmeasure to mothers not completing these question-naires as they were allowed to take the FFQs home tocomplete to reduce the time burden associated withdata collection. FFQ questionnaires have known limi-tations including recall bias, and there is the potentialthat the women wanted to report healthy intakes fortheir children, which would mean that the diet qualitywas overestimated. However, this seems unlikely onthe basis of the low observed values for the ARFS andACARFS.Although the data showed maternal diet tobe a strong predictor, this study did not account for anumber of factors that might influence acceptance offoods by the child. These factors include mothers’nutrition knowledge, maternal attitudes towardsweight and food and behaviours involved in childfeeding practices, such as repeated exposure to foods.Therefore, further research in this area would informthe design of targeted interventions. The aim of thisstudy was the association between mother and childdiet quality, and as such, father diet quality was notassessed. However, the importance of father dietquality of the child should not be overlooked and

future studies should examine father–child dietquality associations. This study is strengthened by thelongitudinal dietary intake data for women and theirchildren collected prospectively throughout preg-nancy and post-partum for both mother and child. Inaddition, the AES and the ACAES are validated toolsto assess dietary intake in these participant groups(Hodge et al. 2002; Watson et al. 2009).

The current study demonstrated that maternal dietquality post-partum, not during pregnancy, is predic-tive of the diet quality of their child at age 2–3 years.This suggests that the quality of the mother’s currentdiet influences the respective quality of the child’sdiet. Further research is warranted to investigatewhether dietary intervention in the mother will havebeneficial effects on the diet quality for both motherand child.

Conclusions

Testing the current hypothesis in a larger populationsample is important, given the small sample size in thecurrent study. However despite this, statistically sig-nificant relationships were found that suggest thatfuture research testing interventions to optimizetoddler dietary quality should simultaneously targetthe dietary intake of their mothers. Focus should beon encouraging healthy dietary habits for mothersthrough the inclusion of a wide variety of nutritiousfood. By offering these foods to offspring and model-ling healthy habits, both mothers and their childrenhave the potential to benefit.

Acknowledgements

This study was undertaken as a partial requirementfor the degree of Bachelor of Nutrition and Dietetics(Honours program) at the University of Newcastle,Australia. All authors contributed to reviewing,editing and approving the final version of the paper.We would like to thank research assistant HannahLucas and postdoctoral candidate MichelleBlumfield. We would also like to thank all mothersand children who participated in the WATCH study.



Sources of funding

The WATCH study received funding from the Uni-versity of Newcastle, the Newcastle Permanent Chari-table Foundation and the John Hunter HospitalCharitable Trust. The study sponsors were notinvolved in the research design, implementation orpublication.

Conflicts of interest

The authors declare that they have no conflicts ofinterest.

Contributions

AA wrote the majority of this manuscript and con-tributed to data analysis and interpretation. AH andCC designed the WATCH study, the methods for thissecondary data analysis and contributed to the inter-pretation of results and writing of this manuscript.AH conducted data collection for the WATCH study.CO designed the statistical analysis for this data andCO, AH and AA conducted the statistical analyses.MJ contributed to the writing and revision of themanuscript. All authors contributed to the final revi-sion of the manuscript.

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Supporting information

Additional Supporting Information may be found inthe online version of this article at the publisher’sweb-site:

Table S1. Scoring method for items in the AustralianChild and Adolescent Recommended Food Score.Table S2. Pearson’s rank correlations betweenmothers’ ARFS (total and sub-scales) at various timepoints during pregnancy and their children’sACARFS (total and sub-scales) at ages two and three.

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maternal diet during early childhood, but not pregnancy, predicts diet quality and fruit and...

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