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Ecological factors and childhood eating behaviours at 5 years of age: findings from the ROLO longitudinal birth cohort study



Individual differences in children eating behaviours have been linked with childhood overweight and obesity. The determinants of childhood eating behaviours are influenced by a complex combination of hereditary and ecological factors. This study examines if key ecological predictors of childhood overweight; maternal socio-economic status (SES), children’s screen time, and childcare arrangements, are associated with eating behaviours in children aged 5-years-old.


This is secondary, cross-sectional analysis of the ROLO (Randomized COntrol Trial of LOw glycemic diet in pregnancy) study, using data from the 5-year follow-up (n = 306). Weight, height, and body mass index (BMI) were obtained from mothers and children at the 5-year follow-up. Children’s BMI z-scores were calculated. SES was determined using maternal education level and neighborhood deprivation score. Information on children’s screen time and childcare arrangements were collected using lifestyle questionnaires. Children’s eating behaviours were measured using the Children’s Eating Behaviour Questionnaire (CEBQ). Multiple linear regression, adjusted for potential confounders, assessed associations between maternal SES, screen time and children’s eating behaviours. One-way ANOVA, independent sample t-tests and Spearman’s correlation examined childcare exposure and children’s eating behaviour.


Mothers in the lowest SES group had higher BMI and were younger than those in the highest SES group (p =  < 0.001, p = 0.03 respectively). In adjusted analysis, the lowest SES group was associated with a 0.463-point higher mean score for ‘Desire to Drink’ (95% CI = 0.054,0.870, p = 0.027) and higher ‘Slowness to Eat’ (B = 0.388, 95% CI = 0.044,0.733, p = 0.027) when compared with the highest SES group. Screen time (hours) was associated with higher ‘Food Fussiness’ (B = 0.032, 95% CI = 0.014,0.051, p = 0.001). Those who attended childcare had higher scores for ‘Desire to Drink’(p = 0.046). No relationship was observed between longer duration (years) spent in childcare and eating behaviours.


In this cohort, the ecological factors examined had an influence on children’s eating behaviours aged 5-years-old. Our results illustrate the complexity of the relationship between the child’s environment, eating behaviour and children’s body composition. Being aware of the ecological factors that impact the development of eating behaviours, in the pre-school years is vital to promote optimal childhood appetitive traits, thus reducing the risk of issues with excess adiposity long-term.

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Childhood obesity is a growing problem globally and its etiology is complex. It is because of this complexity that prevention is key, ensuring children get the best start in life. The ecological model of health can be used to map risk factors and better understand their relationship to childhood obesity [1, 2]. This model shows the breadth of etiological factors involved in childhood overweight and obesity, including the impact of parents and family, community demographics and socio-economic status (SES), food intake, physical and sedentary activity and the child’s own personal characteristics (Fig. 1). Many of these same factors are influential in determining how a child’s eating behaviours will evolve [3].

Fig. 1
figure 1

Ecological model of predictors of childhood overweight. Adapted from Davidson and Birch (refererence 1) and reprinted with permssion from Obesity Reviews. (Ref 1; Davidson KK, Birch LL. Childhood overweight: a contextual model and recommendations for future research. Obes Rev. 2001;2(3) 159-71)

Childhood food approach eating behaviours such as food responsiveness and emotional overeating, are most commonly associated with increased weight [4,5,6]. Children with food responsive eating behaviour (heightened responsiveness to food stimuli, regardless of hunger), have higher meal frequency [7] and display more snacking behaviour [8, 9]. The relationship between weight status and food avoidant eating behaviour such as fussy eating behaviour is more complex. Food fussiness is associated with both greater risk of being underweight [6, 10] and being overweight [11]. Children with fussy eating commonly have limited variety in their diet [12] particularly fruit and vegetables, but may overeat other food groups such as carbohydrates and fats [13]. Understanding the robustness of the relationships between aspects of a child’s ecological framework and a child’s eating behaviour is important for future behavioural intervention strategies.

Lower socio-economic status (SES) is a marker for an obesogenic environment which can impact eating behaviours [14]. Recent data from Ireland and the UK have reported that children from the most deprived backgrounds are more likely to have overweight or obesity than those from more advantaged backgrounds [15, 16]. It is postulated that those from lower SES households may be vulnerable to appetite irregularities due to the combined effect of lower breastfeeding rates and the adversity and stress associated with being disadvantaged [17]. Eating behaviours such as satiety responsiveness, food preference, and selective attention towards food have been shown to be impacted by being from a disadvantaged household [18, 19]. SES is also known to influence type and quality of dietary intake in childhood [20]. A longitudinal study of 8–12-year-old children and their parents, demonstrated that children of mothers with higher educational attainment ate more fruit, vegetables and included daily breakfast more often [21]. The relationship between SES and food avoidant eating behaviours, such as fussy eating, is less clear, with contrasting results as to how SES relates to eating style [10, 22].

Family environment and particularly parental SES has been shown to influence screen time exposure in young children [23]. Furthermore, excessive screen time has been linked with unhealthy eating behaviours in children aged 5–6 years old and young adolescents (11–12 years old), such as increased snacking on energy dense foods and low fruit and vegetable intake [24, 25]. Evidence relating to the impact of screen time on eating behaviours, such as fussy eating, is lacking.

Societal changes over the past three decades have led to large proportions of children spending time in childcare in their preschool years. International data reports that approximately 50% of 3-to-6-year old’s and 25% of infants under 3 years old are exposed to some out of home childcare [26, 27]. Childcare attendance in both formal (preschool/creche-based) and informal (relative/family) settings have been associated with childhood overweight and obesity [27,28,29]. However, findings are inconsistent and multifaceted [30, 31]. Childcare classified as ‘Informal’ has been linked to early introduction to solid food, less physical activity and excess adiposity [30]. Children can spend a considerable amount of time in childcare thus making it a key location for the establishment of eating behaviours. The role of peers in the preschool setting has been found to be influential in shaping dietary patterns and the physical activity levels of their fellow counterparts [32]. In addition, other factors such as the attitudes and behaviours of the childcare providers towards food and their responsiveness to the child’s eating style have to be considered.

Overall, there is a paucity of literature in relation to how the ecological factors in which a child is embedded within influence eating behaviours, and consequently weight status. To address this gap, the current study’s primary aim was to investigate three components of the ecological model of predictors of childhood overweight; namely maternal SES, child screen time exposure and childcare arrangements, and their associations with children’s eating behaviours in children aged 5 years old. To further elucidate the potential influence of ecological factors on child eating behaviours, a secondary aim explored maternal characteristics and child early feeding across SES groups.


Study details

The ROLO (randomized control trial of low glycemic index diet in pregnancy) study is an ongoing longitudinal birth cohort. The primary study took place in the National Maternity Hospital, Dublin, Ireland from 2007–2011 and focused on a low glycemic index diet during pregnancy, with the aim of preventing the recurrence of fetal macrosomia [33]. Findings from the primary ROLO study have been published elsewhere [33]. The mothers and infants from this study (n = 759) have been followed-up as part of the ROLO longitudinal birth cohort study. Participants have been followed-up at numerous time points including at 3 and 6 months, 2 years and when children were aged 5 years old for the ROLO Kids study. ROLO Kids consisted of 401 mother–child dyads and will be the focus for this cross-sectional analysis. Of the 401 children that returned at the 5-year-old follow-up, complete data on child eating behaviours at 5 years old was available for 306 participants, resulting in a final sample of 306 mother–child dyads. A study flow chart detailing the progression of the ROLO study and participant numbers at each follow-up can be seen in Fig. 2. Ethical approval was granted by Our Lady’s Children’s Hospital, Dublin (OLCHC) and the National Maternity Hospital (NMH), Dublin Ethics Committees (Ethics reference number: GEN/279/12).

Fig. 2
figure 2

Flow chart of the ROLO longitudinal birth cohort study. ROLO; Randomized control trial of low glycaemic index diet in pregnancy

Child anthropometry at 5-year follow-up

Weight was measured to the nearest 0.1 kg, using a calibrated stand-on digital weighing scale (SECA 813, Hamburg, Germany). Standing height was measured, without shoes, with head aligned in the Frankfort plane, using a free-standing stadiometer (SECA 217, Hamburg, Germany) and measurements were recorded to the nearest 0.1 cm. Body mass index (BMI) was calculated as kilogram per meter squared (kg/m2). Children’s BMI scores were converted to standardized z-scores according to the 1990 UK age- and sex-specific reference data using Excel LMS Growth macro [34, 35]. BMI z-scores were categorized using the World Health Organization criteria for children aged 5 to 19 years [36]. All measurements were taken at the 5-year-old follow-up study day as per study protocol and were carried out by trained researchers.

Children's eating behaviour at 5-year follow-up

Children’s eating behaviours were measured at 5 years old using the Children’s Eating Behaviour Questionnaire (CEBQ) [37]. The CEBQ is a 35-item, parent reported, validated psychometric tool, developed to capture individual differences in eating styles that may contribute to both underweight and overweight in children [37]. The CEBQ has been validated in a British cohort of children aged 4–5 years old as an accurate measurement of child eating behaviours, displaying good internal consistency and good test–retest reliability [38]. To date the CEBQ has not been validated in an Irish cohort. The CEBQ has eight subscales, four that measure food approach eating behaviours and four that measure food avoidant eating behaviours. The food approach eating behaviour subscales include: ‘Food Responsiveness’ (5 items), measuring heightened responsiveness to external food cues, ‘Enjoyment of Food’ (4 items) measuring a general liking for eating, ‘Emotional Overeating’ (4 items) measuring food intake in response to negative emotions such as anger or anxiety and ‘Desire to Drink’ (3 items) which measures an increased desire to drink, particularly of sugary drinks. The food avoidant behaviour subscales include: ‘Satiety Responsiveness’ (5 items) which measures the degree of self-regulation of food consumed by the child, based on the sensation of feeling full, ‘Emotional Undereating’ (4 items) measuring a decrease in food intake due to negative emotions such as anger or anxiety, ‘Slowness in Eating’ (4 items) measuring the speed at which a child eats, with slowness representing disinterest in food, and ‘Food Fussiness’ (6 items) which measures, a lack of interest in food, or for trying new foods. Responses to the CEBQ statements are scored on a 5-point Likert scale (1 = Never, 5 = Always) with five statements within the CEBQ being reverse scored due to opposite phrasing. To determine the score for each subscale, the items within the subscale were summed and its mean calculated by dividing by the number of items within the subscale. A higher score indicates the child is more likely to express this eating behaviour.

Assessment of screen time

Screen time was assessed at 5 years old, using the Children’s Leisure Activities Study Survey (CLASS) questionnaire, which is validated for children aged 5–6 years old [39]. Screen time was defined as a combination of three activities; watching television/video games, computer games, and internet. Mothers were asked to report on the amount of screen time minutes for a typical week (Monday to Friday) and a typical weekend (Saturday and Sunday). Screen time was calculated in minutes per week, and analyzed in hours per week.

Parenting, feeding and parent characteristics

Maternal weight was measured at the same visit as her child’s and used the same methodology. Maternal education was self-reported and recorded at recruitment for the ROLO pregnancy study. Mothers selected one of the following categories; ‘no schooling’; ‘primary education only’, ‘some secondary level education’, ‘complete secondary level education’, ‘some third level education (certificate/diploma)’ or ‘complete third level education (higher-level degree)’.

Mothers reported breastfeeding duration retrospectively, at 6 months, 2 years and 5 years postnatally. At each timepoint, mothers reported if they had breastfed or not and for how long. At the 2 year and 5-year follow-up mothers reported the age (weeks) their infant had commenced solids. A variable was created to indicate if their child had started solids as per national recommendations [40], that is; that complementary foods are not introduced before four months (17 weeks), but should not be delayed beyond six months (26 weeks).

Community and demographic factors

The Pobal Haase & Pratschke Deprivation Index (HP Index) was used to allocate a deprivation score as per the participants address or small area [41]. Participants addresses were obtained during the ROLO pregnancy study (2007–2011). The HP Index is derived from data from the ‘2011 Census of Population in Ireland’, and provides information on a combination of three dimensions of relative affluence and deprivation, specifically demographic profile, social class composition and labor market supply and demand. The HP Index data is normally distributed; with the mean fixed at 0. Thus, a negative score (below 0) is classified within the disadvantaged categories and a positive score (above 0) being grouped within the advantaged categories. SES was determined by creating a composite variable, using maternal education level and Pobal HP Index [41]. Four SES categories were created based on level of education and neighborhood deprivation index: ‘Third level and Advantaged’, ‘Third level and Disadvantaged’, ‘Less than third level and Advantaged’ and ‘Less than third level and Disadvantaged’.

Information on childcare was obtained from maternal reported lifestyle questionnaires at the 5-year follow-up. In the current analysis, childcare attendance was defined as non-parental care, which may have been in the home or outside the home. Mothers were asked an open-ended question about whether their child attended childcare or not. Categories provided for mothers to describe their childcare arrangements included; crèche or preschool (formal), childminder in the home, childminder outside the home, relative in the home, relative outside the home (informal) or other (asked to specify). Duration of childcare attendance was calculated by subtracting the age of childcare commencement from the age of the child at the 5-year examination. The childcare questionnaire asked whether food was provided, and this was classified into five groups; ‘Yes, all meals’, ‘Yes, main meals only’, ‘Yes, snacks only’, ‘No meals’ or ‘Don’t know’.

Statistical analysis

Continuous data were tested for normality using the Kolmogorov–Smirnov test and visual inspection of histograms. Normally distributed variables were reported as mean and standard deviation (SD). Non-parametric variables were reported as median and interquartile range (IQR). Eating behavior variables were normally distributed and parametric tests were used. One-way ANOVA with post-hoc Tukey’s tests were used to explore crude mean differences in maternal and child characteristics across SES groups. Maternal BMI was non-normally distributed; therefore, Kruskal–Wallis and Mann Whitney U tests were used to examine these characteristics across SES. Chi-squared test for independence was completed to examine breast feeding exposure across SES. Unadjusted and adjusted linear regression analysis were performed to examine associations between maternal SES and children’s eating behaviours at age 5. For this analysis dummy variables were created, with the largest category ‘Third level and Advantaged’ used as the reference variable. Conditions of independence, linearity, normality and homoscedasticity were tested and met prior to all linear regression analyses. Cronbach α was performed on each subscale of the CEBQ to assess the internal consistency of each subscale in our cohort.

Total screen time exposure was analyzed in hours per week. Adjusted and non-adjusted linear regression were completed to examine associations between children’s screen time and eating behaviours.

Independent sample t-tests examined differences in eating behaviours and child’s body composition for children who had attended childcare and those who did not. Meals provided in childcare were recategorized into two groups, ‘food provided’ and ‘no food provided’. Independent sample t-tests were completed to examine differences between these two groups. Spearman’s correlations were completed to assess relationships between duration of time spent in childcare and children’s eating behaviours. Type of childcare attendance was stratified into 3 groups – ‘formal’ (creche/preschool), ‘informal’ (nanny or relative inside or outside the home) and ‘mixed’ – combination of both. One-way ANOVA assessed differences in eating behaviour and children’s body composition across type of childcare.

All multiple regression models were adjusted for maternal BMI at the 5-year follow-up, child breastfeeding exposure, whether the child met national guidelines for age starting solids or not, child age at 5-year follow up, child sex, and original RCT allocation group. Maternal SES was also included as a confounder in the screen time regression models. A p-value of < 0.05 was considered statistically significant. Statistical analyses were completed using IBM Statistical Package for Social Sciences (SPSS) for Windows, version 24.0. Armonk, NY: IBM, Corp.


The study group characteristics are presented in Table 1. At the 5-year follow-up 61.8% of women had completed third level education or above and lived in an advantaged area. Children’s mean age at follow-up was 5.1 years, with 47% males and 53% females. Of these, 24% had a BMI z-score in the overweight or obese range. Median screen time exposure for children was 11.0 h per week. At the 5-year follow-up 89.5% of children had attended childcare, with median childcare exposure 4.1 years (Table 1).

Table 1 General characteristics of the ROLO mother and child dyads at the 5-year follow-up

A total of 306 mothers completed a CEBQ for their child with internal reliability coefficients (Cronbach’s α) ranging from 0.695 to 0.928, thus all questions were included in the analysis. The Cronbach α value for each factor is as follows: ‘Food Responsiveness’ (5 items) 0.822, ‘Emotional Overeating’ (4 items) 0.758, ‘Enjoyment of Food’ (4 items), 0.890, ‘Desire to Drink’ (3 items), 0.864, ‘Satiety Responsiveness’ (5 items), 0.779, ‘Slowness Eating’ (4 items) 0.792, ‘Emotional Undereating’ (4 items) 0.695, and ‘Food Fussiness’ (6 items), 0.928. Mean scores and standard deviations (SD) for children’s eating behaviours are described in Table 1.

Differences between the ‘Third level and Advantaged’ and ‘Less than third level and Disadvantaged’ SES groups were evident for maternal age (Mean 39.4 ± 3.21 versus 35.82 ± 5.24, p = 0.001) (Additional file 1, Table 1). Maternal BMI at 5-year follow-up was lower in the ‘Third level and Advantaged’ group compared with ‘Less than third level and Advantaged’ and ‘Less than third level and Disadvantaged’ (Median 24.35, IQR = 22.42, 26.85 versus Median 25.58, IQR = 23.78,29.82; Median 25.04, IQR = 23.73, 29.96, p = 0.03 respectively) (Additional file 1, Table 1). ‘Desire to Drink’ scores were lower for children of mothers in the ‘Third level and Advantaged’ SES category compared with the ‘Less than third level and Disadvantaged’ SES category (Mean 2.51 ± 0.81 versus Mean 3.01 ± 1.08, p = 0.01) (Additional file Table 1). A chi squared test for independence indicated a significant difference across SES groups between those who had breastfed or not (χ2(3, n = 244) = 48.72, p = 0.001), with lower breastfeeding exposure in the ‘Less than third level and Disadvantaged’ group compared with the ‘Third level and Advantaged’ group (Additional file 2, Table 2).

In adjusted analysis, ‘Less than third level and Disadvantaged’ and ‘Less than third level and Advantaged’ were associated with higher ‘Desire to Drink’ (B = 0.462, 95% CI = 0.054, 0.870, p = 0.027; B = 0.297, 95% CI = 0.026,0.568, p = 0.032 respectively) when compared to ‘Third level and Advantaged’ (Table 2). ‘Less than third level and Disadvantaged’ was associated with higher ‘Slowness to Eat’ (B = 0.388, 95% CI = 0.044, 0.733, p = 0.027) when compared with ‘Third level and Advantaged’ (Table 2).

Table 2 Association between maternal socio-economic status at time of birth and children’s eating behaviours at aged 5 years old

In adjusted regression models, child screen time exposure (hours) was associated with higher ‘Food Fussiness’ (B = 0.032, 95% CI = 0.014, 0.051, p = 0.001) (Table 3). No other associations were observed between screen time exposure and any other eating behaviour.

Table 3 Association between children’s screen time exposure and children’s eating behaviour at 5 years old

Of the children who attended childcare, 59% availed of formal childcare, 31.6% of informal childcare, and 10.3% attended a combination of both formal and informal (Table 1). Children who had attended childcare had higher mean scores for ‘Desire to Drink’ than those who had never attended childcare (Mean 2.70 ± 0.94 versus Mean 2.42 ± 0.72, p = 0.046) (Table 4). No differences were seen in child eating behaviours across type of childcare. In correlation coefficient analysis, no relationships were observed between duration of time spent in childcare and childhood eating behaviours (Additional file 3, Table 3). In relation to provision of food at childcare, 57% of children received some food in childcare, 40.6% received no food, 2.4% did not know (Table 1). No differences were observed in eating behaviours between those who received food compared to those who did not.

Table 4 Children’s eating behaviours across childcare attendance in 5 years old children


The present study explored three predictors of childhood overweight, which feature in the ecological model; SES, screen time exposure and childcare arrangements, and their associations with children’s eating behaviours at 5 years old. We also explored maternal characteristics, child early feeding and eating behaviours across SES groups. Our findings indicate that children of mothers with lower than third level education and living in a disadvantaged area exhibited higher ‘Desire to Drink’ and ‘Slowness to Eat’ scores when compared to children of mothers who had completed third level or above education and lived in an advantaged area. Screen time exposure was positively associated with ‘Food Fussiness’. At 5 years old, attending childcare was positively associated with ‘Desire to Drink’.

Apart from ‘Desire to Drink’, no other food approach eating behaviours were associated with SES in this cohort. We had expected to observe associations between SES and other food approach appetitive traits, such as ‘Food Responsiveness’ and ‘Enjoyment of Food’ in view of an already established connection between these eating behaviours and childhood overweight and obesity [42], and the higher prevalence of overweight and obesity in disadvantaged backgrounds [15, 16]. However, it is important to highlight recent evidence showing a strong genetic component in the development of these particular appetitive traits [43, 44]. Twin studies have demonstrated that ‘Food Responsiveness’ is highly heritable [45, 46] however, it has also been shown that the behavioural manifestation of heightened responsiveness to food cues is also dependent on the contribution of environmental factors [47, 48]. ‘Less than third level and Disadvantaged’ was positively associated with a 1.30-point increase in mean score for ‘Desire to Drink’ compared to ‘Third level and Advantaged’. Similar results have been shown in previous studies in low-income families [49, 50]. In the Generation R study, lower SES at 5 years was associated with higher ‘Desire to Drink’, higher ‘Food Responsiveness’ and higher ‘Emotional Overeating’[49]. Furthermore, a study from the UK, observed associations between higher ‘Desire to Drink’ and increased intake of sugar sweetened beverages (SSB) [51]. Dietary patterns that include high intakes of SSB have been linked with childhood obesity [52, 53], increased food intake [51] and a positive energy balance [54]. However, in the CEBQ, the construct of ‘Desire to Drink’ does not provide information on the actual type of beverage the child was likely to consume or request. Further research exploring the relationship between dietary patterns across SES groups and ‘Desire to drink’ is warranted.

Our cohort reflected current national data of lower rates of breastfeeding in lower SES groups [55]. In previous research from the ROLO cohort, an association was observed between lower breastfeeding duration and higher scores for ‘Desire to Drink’ [56]. In the current study, even following adjustment for breastfeeding exposure, a positive association between ‘Less than third level and Disadvantaged’ and ‘Desire to Drink’ remained, suggesting that the association was not influenced by breastfeeding exposure.

The only associations observed between SES and the food avoidant eating behaviours was with ‘Less than third level and Disadvantaged’ and higher mean scores for ‘Slowness Eating’. ‘Slowness Eating’ represents a small appetite and disinterest in food. Evidence shows that children from lower SES households will be exposed to a more ‘obesogenic’ environment with greater access to unhealthy foods, less structured meal times and less parental responsive feeding practices [57, 58]. Research also suggests that lower SES households have higher intakes of SSB [59] and children who drink excessively often do have poorer appetites, as the volume of fluid intake can displace hunger. Previous research has found that fussy eating is more common in lower SES households [10, 60]. However contrasting results have also been demonstrated, showing associations between lower SES and lower scores for food fussiness and higher scores for ‘Food Responsiveness’ ‘Enjoyment of Food’, with no associations observed between SES and ‘Slowness eating [49].

In recent years an environmental factor that is inevitably part of the lives of both adults and children is exposure to screen time. It is recommended that children aged 2–5 years old spend no more than 1 h per day exposed to screens [61, 62]. Our cohort had an average of 1.8 h of screen time per day. Excessive screen time is associated with unhealthy eating behaviours, such as increased snacking on energy dense foods and reduced intake of fruit and vegetables [24, 25]. In the current analysis, none of the food approach eating behaviours were associated with screen time exposure, however, screen time exposure was positively associated with ‘Food Fussiness’. As screen time has been associated with increased snacking or grazing behaviour this may consequentially predispose the child to a poorer appetite at meal times, reduced satiety cues and a less structured meal time environment. A structured meal time environment for children has been proposed as an important strategy for reducing fussy eating and helping a child to recognize their hunger cues. Structured meal times include the provision of a routine, reduced distractions at the meal and having the family present for meals [63]. However, this association potentially could be bi-directional, as children with fussy eating and/or sensory issues may avoid sitting at the table with family for meals and screen time may be used as a distraction technique to help encourage eating. In a previous study of preschool children, parents of children who demonstrated fussy eating behaviour reported that their child had too much screen time [64]. To our knowledge, the association between screen time and fussy eating in children has not been previously demonstrated. However, further research is warranted which includes data on dietary patterns to help fully understand this association between screen time exposure and fussy eating behaviour.

Currently, research is limited on how childcare impacts children’s eating behaviours. Research from the child development field indicates that the quality of childcare is an important determinant of positive or negative childhood developmental outcomes, particularly for those from disadvantaged backgrounds [65]. We observed higher scores for ‘Desire to Drink’ for those who attended childcare versus those who did not. However, when childcare was categorized into formal/informal or a combination of both, no differences were observed. As previously discussed, research on preschool children has demonstrated that the construct of ‘Desire to Drink’ was related to an increased desire for sugar sweetened beverages, and that this was not driven by thirst [51].

A positive finding in our cohort was that no associations were observed between duration of exposure to childcare and any of the food avoidant eating behaviours. To date, limited research is available on the impact of childcare on food avoidant eating behaviours and whether fussy eating perception differs between a child's parent and their childcare provider. In a small study of 3–5-year-old children, video observation of eating patterns at home and in childcare demonstrated that children exhibited more fussy eating behaviour at home than in childcare [66]. The influence of peers may play a role in diminishing food avoidant eating behaviour in the childcare setting, as children tend to imitate and learn from their peers [32]. Longitudinal data is required to examine how children’s peers within the childcare setting may influence eating behaviours over time.

Strengths of this study include the use of a validated questionnaire to measure childhood eating behaviours. Combining maternal educational level and neighborhood deprivation (HP Index) to form an education-deprivation variable provided a holistic measure of maternal SES, that allowed for the categorization of the most advantaged and most disadvantaged groups within the study cohort. The HP index is also specific to the population of Ireland. The availability of maternal information and demographics from the original ROLO pregnancy study, in combination with the 5-year-old follow-up data, allowed for adjustment of important potential confounders in all analyses. All anthropometric measurements were measured by trained researchers. This study has a number of limitations. This study is cross-sectional in design, which precludes ability to infer causality. Selection bias may have been present as all mothers from the original ROLO pregnancy study were healthy and on their second pregnancy. Another limitation is that the majority of participants were in the higher SES group, with more than half of the original ROLO cohort having achieved a third-level education or more. Therefore, this sample may not be fully representative of the general population. Data provided by mothers on screen time exposure could have been strengthened if the questions on screen time had been repeated on several occasions and had included a request to mothers to keep an example diary. The CEBQ and the CLASS questionnaires have not been validated in an Irish population. Another limitation of this study is that the CEBQ is a parent reported questionnaire and therefore responses may be subject to social desirability bias. Although we adjusted for key confounders, there are other important potential confounders that were not included such as the child’s physical activity levels, parental feeding styles and parents eating behaviors. Controlling for these would have strengthened this research.


This study adds insight into how three predictors, of childhood overweight, contained within the ecological framework; maternal SES, child screen time exposure and childcare arrangements, are associated with child eating behaviours in children aged 5 years old. The association between increased ‘Desire to Drink’ and lower SES may point to learned dietary patterns within the home environment. To our knowledge, our finding regarding the association between screen time exposure and food fussiness is novel, and requires further exploration to understand the direction of this association. Understanding how ecological factors, particularly in early childhood, impact a child’s eating behaviors is important in taking a systems approach to prevention of overweight and obesity.

Availability of data and materials

The datasets used and analyzed during the current study are not publicly available in line with ethical approval but are available from the corresponding author on reasonable request.



Socio-economic status


Randomized control trial of low glycemic index diet in pregnancy


Children’s Eating Behaviour Questionnaire


  1. Davison KK, Birch LL. Childhood overweight: a contextual model and recommendations for future research. Obes Rev. 2001;2(3):159–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Birch LL, Ventura AK. Preventing childhood obesity: what works? Int J Obes. 2009;33(S1):S74–81.

    Article  Google Scholar 

  3. De Cosmi V, Scaglioni S, Agostoni C. Early Taste Experiences and Later Food Choices. Nutrients. 2017;9(2):107.

    Article  PubMed Central  CAS  Google Scholar 

  4. McCarthy EK, Cn Chaoimh, Murray DM, Hourihane JOB, Kenny LC, Kiely M. Eating behaviour and weight status at 2 years of age: data from the Cork BASELINE Birth Cohort Study. Eur J Clin Nutr. 2015;69(12):1356–9.

    Article  CAS  PubMed  Google Scholar 

  5. Domoff SE, Miller AL, Kaciroti N, Lumeng JC. Validation of the Children’s Eating Behaviour Questionnaire in a low-income preschool-aged sample in the United States. Appetite. 2015;95:415–20.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Herle M, De Stavola B, Hubel C, Santos Ferreira DL, Abdulkadir M, Yilmaz Z, et al. Eating behavior trajectories in the first 10 years of life and their relationship with BMI. Int J Obes. 2020;44(8):1766–75.

    Article  Google Scholar 

  7. Syrad H, Johnson L, Wardle J, Llewellyn CH. Appetitive traits and food intake patterns in early life. Am J Clin Nutr. 2016;103(1):231–5.

    Article  CAS  PubMed  Google Scholar 

  8. Rudy E, Bauer KW, Hughes SO, O’Connor TM, Vollrath K, Davey A, et al. Interrelationships of child appetite, weight and snacking among Hispanic preschoolers. Pediatr Obes. 2018;13(1):38–45.

    Article  CAS  PubMed  Google Scholar 

  9. Carnell S, Pryor K, Mais LA, Warkentin S, Benson L, Cheng R. Lunch-time food choices in preschoolers: Relationships between absolute and relative intakes of different food categories, and appetitive characteristics and weight. Physiol Behav. 2016;162:151–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cardona Cano S, Tiemeier H, Van Hoeken D, Tharner A, Jaddoe VWV, Hofman A, et al. Trajectories of picky eating during childhood: A general population study: Picky Eating Trajectories. Int J Eat Disord. 2015;48(6):570–9.

    Article  PubMed  Google Scholar 

  11. Finistrella V, Manco M, Ferrara A, Rustico C, Presaghi F, Morino G. Cross-Sectional Exploration of Maternal Reports of Food Neophobia and Pickiness in Preschooler-Mother Dyads. J Am Coll Nutr. 2012;31(3):152–9.

    Article  PubMed  Google Scholar 

  12. Fildes A, Mallan KM, Cooke L, Jaarsveld CHMv, Llewellyn CH, Fisher A, et al. The relationship between appetite and food preferences in British and Australian children. Int J Behav Nutr Phys Act. 2015;12(1):116.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Tharner A, Jansen P, Kiefte-de Jong J, Moll H, Ende J, Jaddoe V, et al. Toward an operative diagnosis of fussy/picky eating: A latent profile approach in a population-based cohort. Int J Behav Nutr Phys Act. 2014;11(1):14.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Gose M, Plachta-Danielzik S, Willié B, Johannsen M, Landsberg B, Müller MJ. Longitudinal influences of neighbourhood built and social environment on children’s weight status. Int J Environ Res Public Health. 2013;10(10):5083–96.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Public Health England, NCMP local authority profile. London: Public Health England; 2018. Available from Accessed 30 Nov 2021.

  16. Mitchell L, Bel-Serrat S, Stanley I, Hegarty T, Mc Cann L, Mehegan J, et al. The Childhood Obesity Surveillence Initiative (COSI) in the Republic of Ireland. Findings from 2018 and 2019. 2020.

  17. Ogbo FA, Eastwood J, Page A, Arora A, McKenzie A, Jalaludin B, et al. Prevalence and determinants of cessation of exclusive breastfeeding in the early postnatal period in Sydney, Australia. Int Breastfeeding J. 2016;12(1):16.

    Article  Google Scholar 

  18. Lumeng JC, Miller A, Peterson KE, Kaciroti N, Sturza J, Rosenblum K, et al. Diurnal cortisol pattern, eating behaviors and overweight in low-income preschool-aged children. Appetite. 2014;73:65–72.

    Article  PubMed  Google Scholar 

  19. Michaud A, Vainik U, Garcia-Garcia I, Dagher A. Overlapping Neural Endophenotypes in Addiction and Obesity. Front Endocrinol (Lausanne). 2017;8:127.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Scaglioni S, De Cosmi V, Ciappolino V, Parazzini F, Brambilla P, Agostoni C. Factors Influencing Children’s Eating Behaviours. Nutrients. 2018;10(6):706.

    Article  PubMed Central  Google Scholar 

  21. Ansem W, Schrijvers C, Rodenburg G, Mheen D. Maternal educational level and children’s healthy eating behaviour: Role of the home food environment (cross-sectional results from the INPACT study). Int J Behav Nutr Phys Act. 2014;11(1):113.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Taylor CM, Wernimont SM, Northstone K, Emmett PM. Picky/fussy eating in children: Review of definitions, assessment, prevalence and dietary intakes. Appetite. 2015;95:349–59.

    Article  PubMed  Google Scholar 

  23. Cameron AJ, Spence AC, Laws R, Hesketh KD, Lioret S, Campbell KJ. A Review of the Relationship Between Socioeconomic Position and the Early-Life Predictors of Obesity. Curr Obes Rep. 2015;4(3):350–62.

    Article  PubMed  Google Scholar 

  24. Pearson N, Griffiths P, Biddle SJ, Johnston JP, McGeorge S, Haycraft E. Clustering and correlates of screen-time and eating behaviours among young adolescents. BMC Public Health. 2017;17(1):533.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Pearson N, Biddle SJH, Griffiths P, Johnston JP, Haycraft E. Clustering and correlates of screen-time and eating behaviours among young children. BMC Public Health. 2018;18(1):753.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Organization for Economic Cooperation and Development (OECD), Enrolment in Childcare and Preschools, social profiles and data. USA: OECD; 2013. Available from Accessed 06 Dec 2021.

  27. Pearce A, Li L, Abbas J, Ferguson B, Graham H, Law C, et al. Is childcare associated with the risk of overweight and obesity in the early years? Findings from the UK Millennium Cohort Study. Int J Obes. 2010;34(7):1160–8.

    Article  CAS  Google Scholar 

  28. McDonnell T, Doyle O. Maternal Employment Childcare and Childhood Overweight during Infancy. School of Economics: University College Dublin; 2014.

    Google Scholar 

  29. Scully H, Alberdi G, Seguardo R, McNamara A, Lindsay K, Horan M, et al. Childcare exposure influences childhood adiposity at 2 years: Analysis from the ROLO study. Child Obes. 2016;2016:1–9.

    Google Scholar 

  30. Alberdi G, McNamara AE, Lindsay KL, Scully HA, Horan MH, Gibney ER, et al. The association between childcare and risk of childhood overweight and obesity in children aged 5 years and under: a systematic review. Eur J Pediatr. 2016;175(10):1277–94.

    Article  PubMed  Google Scholar 

  31. Costa S, Adams J, Phillips V, Benjamin Neelon SE. The relationship between childcare and adiposity, body mass and obesity-related risk factors: protocol for a systematic review of longitudinal studies. Syst Rev. 2016;5(1):141.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ward S, Bélanger M, Donovan D, Boudreau J, Vatanparast H, Muhajarine N, et al. “Monkey see, monkey do”: Peers ’ behaviors predict preschoolers ’ physical activity and dietary intake in childcare centers. Prev Med. 2016;97:33–9.

    Article  Google Scholar 

  33. Walsh JM, McGowan CA, Mahony R, Foley ME, McAuliffe FM. Low glycaemic index diet in pregnancy to prevent macrosomia (ROLO study): randomised control trial. BMJ. 2012;345:e5605.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. British Med J. 2000;320:1240–3.

    Article  CAS  Google Scholar 

  35. LMS Growth -a Microsoft Excel add-in to assess growth references based on the LMS method [Internet]. 2012.

  36. de Onis M. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85(9):660–7.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Wardle J, Guthrie CA, Sanderson S, Rappoport L. Development of the Children’s Eating Behaviour Questiionaire. J Clin Child Psychol. 2001;42:963–70.

    Article  CAS  Google Scholar 

  38. Carnell S, Wardle J. Measuring behavioural susceptibility to obesity: Validation of the child eating behaviour questionnaire. Appetite. 2007;48(1):104–13.

    Article  PubMed  Google Scholar 

  39. Telford A, Salmon J, Jolley D, Crawford D. Reliability and validity of physical actoivity questionaires for children: The Childrens Leisure Activities Study Survey (CLASS). Pediatr Exerc Sci. 2004;16(1):64–78.

    Article  Google Scholar 

  40. Health Service Executive. Weaning starting your baby on solid food Ireland. 2019. Available from:

  41. The 2011 Pobal HP Deprivation Index for Small Areas (SA). Introduction and Reference Tables [Internet]. 2012. Available from: [Cited 01/02/2022].

  42. Steinsbekk S, Wichstrøm L. Predictors of Change in BMI From the Age of 4 to 8. J Pediatr Psychol. 2015;40(10):1056–64.

    Article  PubMed  Google Scholar 

  43. van Jaarsveld CHM, Boniface D, Llewellyn CH, Wardle J. Appetite and Growth: A Longitudinal Sibling Analysis. JAMA Pediatr. 2014;168(4):345–50.

    Article  PubMed  Google Scholar 

  44. Llewellyn CH, Trzaskowski M, van Jaarsveld CHM, Plomin R, Wardle J. Satiety Mechanisms in Genetic Risk of Obesity. JAMA Pediatr. 2014;168(4):338–44.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Llewellyn CH, Van Jaarsveld CH, Johnson L, Camell S, Wardle J. Nature and nurture in infant appetite: analysis of the Gemini twin birth cohort. Am J Clin Nutr. 2010;91(5):1172–9.

    Article  CAS  PubMed  Google Scholar 

  46. Llewellyn CH, Fildes A. Behavioural Susceptibility Theory: Professor Jane Wardle and the Role of Appetite in Genetic Risk of Obesity. Curr Obes Rep. 2017;6(1):38–45.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Wardle J, Carnell S. Appetite is a Heritable Phenotype Associated with Adiposity. Ann Behav Med. 2009;38(S1):25–30.

    Article  Google Scholar 

  48. Schrempft S, van Jaarsveld CHM, Fisher A, Herle M, Smith AD, Fildes A, et al. Variation in the Heritability of Child Body Mass Index by Obesogenic Home Environment. JAMA Pediatr. 2018;172(12):1153–60.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Kininmonth AR, Smith AD, Llewellyn CH, Fildes A. Socioeconomic status and changes in appetite from toddlerhood to early childhood. Appetite. 2020;146:104517.

    Article  PubMed  Google Scholar 

  50. Lora KR, Hubbs-Tait L, Ferris AM, Wakefield D. African-American and Hispanic children’s beverage intake: Differences in associations with desire to drink, fathers’ feeding practices, and weight concerns. Appetite. 2016;107:558–67.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Sweetman C, Wardle J, Cooke L. Soft drinks and “desire to drink” in preschoolers. Int J Behav Nutr Phys Act. 2008;5(1):60.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Bucher Della Torre SMPHRD, Keller AMPHRD, Laure Depeyre JRD, Kruseman MMPHRD. Sugar-Sweetened Beverages and Obesity Risk in Children and Adolescents: A Systematic Analysis on How Methodological Quality May Influence Conclusions. J Acad Nutr Diet. 2015;116(4):638–59.

    Article  PubMed  Google Scholar 

  53. Liberali R, Kupek E, Altenburg de Assis M. Dietary patterns and childhood obesity risk: A systematic review. Childhood Obesity. 2020;16(2):70–85.

    Article  PubMed  Google Scholar 

  54. Bennett LJ, Totosy de Zepetnek JO, Brett NR, Poirier K, Guo Q, Rousseau D, et al. Effect of Commercially Available Sugar-Sweetened Beverages on Subjective Appetite and Short-Term Food Intake in Girls. Nutrients. 2018;10(4):394.

    Article  PubMed Central  CAS  Google Scholar 

  55. Purdy J, McAvoy H, Cotter N. Breastfeeding on the island of Ireland. Dublin: Institute of Public Health in Ireland; 2017.

    Google Scholar 

  56. Yelverton CA, Geraghty AA, O Brien EC, Killeen S, Horan MC, Donnelly JM, et al. Breastfeeding and maternal eating behaviours are associated with child eating behaviours: findings from the ROLO Kids Study. Eur J Clin Nutr. 2021;75(4):670–9.

  57. Rodgers RF, Paxton SJ, Massey R, Campbell KJ, Wertheim EH, Skouteris H, et al. Maternal feeding practices predict weight gain and obesogenic eating behaviors in young children: a prospective study. Int J Behav Nutr Phys Act. 2013;10(1):24.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Gross RS, Mendelsohn AL, Fierman AH, Racine AD, Messito MJ. Food Insecurity and Obesogenic Maternal Infant Feeding Styles and Practices in Low-Income Families. Pediatrics (Evanston). 2012;130(2):254–61.

    Article  Google Scholar 

  59. Bolt-Evensen K, Vik FN, Stea TH, Klepp K-I, Bere E. Consumption of sugar-sweetened beverages and artificially sweetened beverages from childhood to adulthood in relation to socioeconomic status - 15 years follow-up in Norway. Int J Behav Nutr Phys Act. 2018;15(1):8.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Gibson EL, Cooke L. Understanding Food Fussiness and Its Implications for Food Choice, Health, Weight and Interventions in Young Children: The Impact of Professor Jane Wardle. Curr Obes Rep. 2017;6(1):46–56.

    Article  PubMed  Google Scholar 

  61. Health Service Executive. Screen time and young children. Dublin; 2018. Available from:

  62. AAP Council On Communications and Media. Media Use in School-Aged Children and Adolescents. Pediatrics (Evanston). 2016;138(5):e20162592.

  63. Finnane JMB, Jansen EP, Mallan KMP, Daniels LAP. Mealtime Structure and Responsive Feeding Practices Are Associated With Less Food Fussiness and More Food Enjoyment in Children. J Nutr Educ Behav. 2016;49(1):11-8.e1.

    Article  PubMed  Google Scholar 

  64. Sandvik P, Ek A, Somaraki M, Hammar U, Eli K, Nowicka P. Picky eating in Swedish preschoolers of different weight status: application of two new screening cut-offs. Int J Behav Nutr Phys Act. 2018;15(1):74.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Melhuish E, Quinn L, Sylva K, Sammons P, Siraj-Blatchford I, Taggart B. Preschool affects longer term literacy and numeracy: results from a general population longitudinal study in Northern Ireland. Sch Eff Sch Improv. 2013;24(2):234–50.

    Article  Google Scholar 

  66. Luchini V, Musaad S, Lee S-Y, Donovan SM. Observed differences in child picky eating behavior between home and childcare locations. Appetite. 2017;116:123–31.

    Article  PubMed  Google Scholar 

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The authors would like to thank all the ROLO participants for their involvement and all the staff of the National Maternity Hospital and the Perinatal Research Centre.


This study was supported by the Health Research Board, Ireland, the Health Research Centre for Health and Diet Research, The National Maternity Hospital Medical Fund and the European Union’s Seventh Framework Programme (FP7/2007–2013), project Early nutrition under grant agreement no. 289346.

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Authors and Affiliations



FMMA, ECOB and AAG were responsible for the project conception, AD, FMMA, MCC, SOR, CMcD designed the research and analysis plan, ECOB and AAG collected the data, JM collated the database, AD analysed the data and performed statistical analysis, AD wrote the paper and all authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Anna Delahunt.

Ethics declarations

Ethics approval and consent to participate

The ROLO and ROLO Kids studies were carried out in accordance with the Helsinki Declaration of 1975 as revised in 1983 with institutional ethical approval from the National Maternity Hospital in November 2006 for the original ROLO study and for the ROLO Kids 6 month and 2-year follow-up in May 2009. The Current Controlled Trials registration number for the ROLO study was ISRCTN54392969. The ROLO Kids 5-year follow-up were approved by the Ethics (Medical Research) Committee in Our Lady’s Children’s Hospital, Dublin, REC reference: GEN/279/12. Informed written maternal consent was obtained during pregnancy and at each subsequent follow-up.

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Consent to publish has been obtained from participants.

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The authors have no conflicts of interests to declare.

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Supplementary Information

Additional file 1: Table 1.

Maternal and child characteristics according to socio-economic status category at 5-year follow-up.

Additional file 2: Table 2.

Breastfeeding exposure across maternal SES.

Additional file 3: Table 3.

Correlation between duration of time child exposed to childcare and child eating behaviours at 5 years old.

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Delahunt, A., Conway, M.C., O’Brien, E.C. et al. Ecological factors and childhood eating behaviours at 5 years of age: findings from the ROLO longitudinal birth cohort study. BMC Pediatr 22, 366 (2022).

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