Summary
Sugar‐sweetened beverage (SSB) consumption is associated with adverse health outcomes. Improved understanding of the determinants will inform effective interventions to reduce SSB consumption. A total of 46,876 papers were identified through searching eight electronic databases. Evidence from intervention (n = 13), prospective (n = 6) and cross‐sectional (n = 25) studies on correlates/determinants of SSB consumption was quality assessed and synthesized. Twelve correlates/determinants were associated with higher SSB consumption (child's preference for SSBs, TV viewing/screen time and snack consumption; parents' lower socioeconomic status, lower age, SSB consumption, formula milk feeding, early introduction of solids, using food as rewards, parental‐perceived barriers, attending out‐of‐home care and living near a fast food/convenience store). Five correlates/determinants were associated with lower SSB consumption (parental positive modelling, parents' married/co‐habiting, school nutrition policy, staff skills and supermarket nearby). There was equivocal evidence for child's age and knowledge, parental knowledge, skills, rules/restrictions and home SSB availability. Eight intervention studies targeted multi‐level (child, parents, childcare/preschool setting) determinants; four were effective. Four intervention studies targeted parental determinants; two were effective. One (effective) intervention targeted the preschool environment. There is consistent evidence to support potentially modifiable correlates/determinants of SSB consumption in young children acting at parental (modelling), child (TV viewing) and environmental (school policy) levels.
Keywords: Correlates, determinants, sugar‐sweetened beverage, systematic review, young children
Abbreviations
- BMI
body mass index
- EPPI
evidence for policy and practice information
- PROSPERO
International Prospective Register for Systematic Reviews
- SSB
sugar‐sweetened beverage
- XS
cross‐sectional
Introduction
Forty‐three million children aged 0–5 years are obese or overweight worldwide, and the prevalence of obesity in children is estimated to rise from 4.2% in 1990 to 9.1% in 2020 1. Childhood obesity has important consequences for health and well‐being during childhood and also in later adult life 2. According to the National Child Measurement Programme for England, over a fifth of children (22.2%) aged 4–5 years were overweight or obese on school entry. In the final year of primary school, one in three children (33.3%) aged 10–11 years was obese or overweight 3. However, levels of obesity have begun to plateau in Australia, United States and many European countries including United Kingdom 4, 5, 6, 7, 8, 9. A recent study in the United States reported that a child's weight status is set by age 5 and tracks throughout childhood, as nearly half of children who became obese by the eighth grade were already overweight when they started school 10.
Several cross‐sectional (XS) 11, 12 and prospective studies 13, 14 have described the association between sugar‐sweetened beverage (SSB) consumption and obesity in young children. Moreover, recent systematic reviews on experimental evidence show that reducing SSB consumption in young children is successful in reducing obesity 15, 16. Studies additionally show that consumption of SSBs in young children is a risk factor for overall poor diet quality 17 and oral health 18, 19. In addition to weight gain and poor oral health, higher consumption of SSBs is associated with development of metabolic syndrome and cardio‐metabolic risk factors such as type 2 diabetes later in life 20, 21, 22.
Therefore, the rising prevalence of childhood obesity poses a major public health challenge in both developed and developing countries due to the increasing burden of chronic non‐communicable diseases 23. Amid controversy with regard to the role of SSB consumption in obesity development/weight gain, there is growing evidence to suggest that decreasing SSB consumption will reduce the prevalence of obesity and obesity‐related chronic diseases. Meanwhile, despite resistance from the beverage industry, several public policies and regulatory strategies to reduce consumption of SSBs are already in place or being developed worldwide 15, 24, 25, 26.
Evidence suggests that unhealthy dietary habits such as SSB consumption are formed during early childhood and stress the need to understand the correlates/determinants influencing these behaviours in children to inform intervention development 27, 28. The socio‐ecological model of health behaviour suggests that an individual's behaviour is influenced by a multitude of correlates/determinants operating at different levels. This systematic review synthesizes quantitative evidence from intervention and observational (prospective cohort and XS) studies on the determinants and correlates of SSB consumption in young children (0–6 years) using the socio‐ecological model.
Methods
In the absence of a standard definition for SSBs, for the purpose of this review, we defined SSBs as beverages that are high in added sugar and add calories to diet 29, 30. The definition includes sweetened milk (flavoured milk or milk alternatives), fruit drinks (sweetened fruit juice), soft drinks (bottles or cans of non‐alcoholic, flavoured, carbonated or non‐carbonated beverages), tea and coffee drinks (sweetened), energy drinks, sports drinks and any other beverages to which sugar (high‐fructose corn syrup, sucrose or table sugar) has been added.
This review is part of a series of systematic reviews of quantitative and qualitative evidence on determinants of obesogenic behaviours in young children (International Prospective Register for Systematic Reviews [PROSPERO] Registration number: CRD42012002881). The overall study design, search and quality assessment strategies are previously described in the published protocol 31. Methods follow those described for the rigorous conduct and reporting of systematic reviews for policy and practice published by the Evidence for Policy and Practice Information (EPPI) Centre 32.
Search strategy, inclusion/exclusion and quality assessment criteria
Further to an iterative scoping stage, with input from experts, a combined search strategy with terms related to population (young children aged 0–6 years), exposure and outcome (fruit and vegetable consumption, SSB and other obesogenic diet consumption, physical activity and sedentary behaviours) were used to identify papers (details in protocol paper 31). Eight electronic databases were searched from inception to June 2014. No language restrictions were applied, but clinical populations were excluded. All identified articles were imported into an Endnote database and after de‐duplication a total yield of 46,876 articles was achieved. Specifically for SSB consumption behaviours, the inclusion/exclusion criteria have been described in Table 1. The quality assessment criteria were based on methods described by the EPPI Centre 32 and presented in Table 2. For intervention studies, eight items were scored focusing on internal validity (e.g. randomization procedure, objective measure of outcome, retention). For observational studies, six items were scored focusing on both internal and external validity. Studies were classified as high, intermediate or low quality based on the number of quality criteria met (for intervention studies: low: ≤2; intermediate: 3–5; high: ≥6; for observational studies: low: ≤2; intermediate: 3–4; high: ≥5).
Table 1.
Inclusion criteria | Exclusion criteria |
---|---|
|
|
RCT, randomized controlled trial; SSB, sugar‐sweetened beverage.
Table 2.
For intervention studies | For observational (prospective cohort and cross‐sectional) studies |
---|---|
Total quality assessment score (maximum of eight) was derived for fulfilment of the following criteria
Studies with small sample size (n < 50) and no control group were considered to provide lower quality evidence and not scored. |
Total quality assessment score (maximum of six) was derived for fulfilment of the following criteria
|
SSB, sugar‐sweetened beverage.
Study selection
Duplicate review of at least a subsample of papers was carried out at each stage of the review process: title and abstract screening (CO, KH, VMP, CS, EMFvS and RL), data extraction (VMP, EMFvS, KKO and RL) and quality assessment (VMP, EMFvS, KKO and RL) to ensure high level of agreement and minimize any reviewer‐related biases.
Full texts of articles appearing to meet the inclusion criteria were retrieved for further review and their status recorded in a pre‐piloted IN/OUT spreadsheet, along with specific study details and reasons for exclusion (for excluded studies). Foreign language papers were translated by native speakers. Articles were re‐examined (CS, EMFvS or RL) if there was uncertainty about inclusion criteria and disagreements were resolved at team meetings.
A total of 286 full‐text papers related to SSB and obesogenic dietary behaviours were identified for further review, of which 35 papers met the inclusion criteria. Nine additional papers were identified, two 33, 34 through correspondence with first authors of included studies and two 35, 36 through hand searching/citation tracking of reference lists of included studies (additionally, five were identified by reviewers). A summary flow chart of the literature identification strategy is presented in Fig. 1.
Data extraction
Extracted data (Supplementary Table S1) from all included papers were entered into a previously piloted data extraction spreadsheet and analysed by one reviewer (VMP). Additional to the included articles, cohort data from control group participants were extracted for one intervention study 34, and two prospective studies also reported on XS associations 37, 38. These three studies have been presented and accounted for as per study design (Fig. 1). Multiple papers reporting data from the same study were extracted and referenced separately but considered as one study.
‘Intervention effect’ was defined as the reported difference in SSB consumption in intervention vs. control groups at follow‐up (adjusted for baseline consumption where relevant); the intervention was considered to have a positive effect if it reduced SSB consumption compared with control group. From prospective cohort studies, the association between the determinant at baseline and change in SSB consumption between baseline and follow‐up was extracted. From XS studies, the reported association between the correlate and SSB consumption at the same time point was extracted.
For quality assurance, all the intervention (n = 13) and cohort (n = 6) studies and a sample of over 10% of XS studies (n = 25) were double reviewed by a second reviewer (RL) to ensure agreement and consistency in data extraction and reporting. Furthermore, all intervention studies (n = 13) were double reviewed and analysed (EMFvS and KKO) to identify target determinants.
Data synthesis
A narrative synthesis (including tables) was undertaken. Because of the heterogeneity between studies (in study quality/design, setting, participant characteristics, behavioural models, measures of correlates/determinants and behaviours) and analyses (proportions, means, beta‐coefficients and odds ratios), meta‐analysis was not appropriate.
We performed a non‐quantitative synthesis of all reported determinants or correlates of SSB consumption grouped according to the levels of the socio‐ecological model. Conceptually similar exposures were combined. For each potential determinant, findings from individual studies were categorized as ‘−’ significantly lower/decreased SSB consumption, ‘0’ no significant association/effect or ‘+’ significantly higher/increased SSB consumption. Consistency across studies was then summarized using a previously applied algorithm 39, 40 labelled as ‘0’ (no association) if supported by 0–33% of individual studies, ‘?’ (indeterminate/possible) if supported by 34–59% and ‘+’ or ‘−’ if supported by 60–100%. Moreover, where four or more studies reported on a potential determinant, double signs were used to indicate greater confidence in the summary (e.g. ‘00’, ‘??’, ‘++’ and ‘−−’).
Results
Study characteristics
Intervention studies
Reports from 13 intervention studies, published between 2007 and 2013, were identified (Supplementary Table S2). Four studies were conducted in the Americas, four in Australia, four in Europe (Belgium n = 2, Spain n = 1, United Kingdom n = 1) and one in Asia. Children's age ranges varied from early infancy to 4–6 years old. Duration of interventions varied from 8 weeks to 4 years, and post‐intervention follow‐up was either immediate (n = 11), 6 months (n = 1) or 4 years (n = 1). Seven intervention studies used a behaviour change theory, four of which showed a significant positive effect in favour of the intervention. Of the six interventions that did not use a behaviour change theory, three studies showed a significant positive effect.
Eight intervention studies 34, 41, 42, 43, 44, 45, 46, 47 targeted multi‐level (child, parents, childcare/preschool setting) determinants of SSB consumption, of which four 43, 44, 46, 47 reduced SSB consumption. Four interventions 33, 48, 49, 50, 51 (one intervention 48, 49 reported results at two time points) exclusively targeted parental determinants and two 33, 51 reduced SSB consumption. One study 52 targeted the preschool environment and reduced SSB servings in the child's lunchbox. No interventions exclusively targeted child determinants of SSB consumption. No intervention studies reported on a mediation analysis to suggest that a change in any particular determinant was associated with change in SSB consumption. Effective studies were set outside of Europe (Australia n = 3, Asia n = 1, United States n = 3). A UK study did not show a significant effect post‐intervention (age 1 year), but reported a significant positive effect at age 4 years 48, 49. Seven studies 33, 45, 46, 47, 48, 49, 50, 51 including one very small pilot study 46 recruited non‐representative populations. Five studies were rated as ‘high’ quality 44, 45, 48, 49, 51, 52; three of these reported significant beneficial effects of the intervention 44, 49, 52.
Observational – prospective cohort studies
Six prospective cohort studies published between 1999 and 2012 were identified (Supplementary Table S3). Three studies were set in the United States 38, 53, 54 and one each in Australia 37, Belgium 34 and Germany 55. Four were in non‐representative populations: two in populations with limited income 28, 43, one with health conscious participants 55 and one had a small sample of Caucasian infants (n = 49) 54. The studies had an average follow‐up period of 2 years and the age range varied from infants up to 6.5 years old. The studies were of intermediate (n = 4) or low (n = 2) quality and investigated a total of seven determinants: three at the individual level (child) and four at the interpersonal (parent/caregiver) level.
Observational – XS studies
We identified 25 XS studies of intermediate (n = 19) and low (n = 6) quality published between 2002 and 2013 (Supplementary Table S4). Nine studies were conducted in the United States 38, 56, 57, 58, 59, 60, 61, 62, 63, two in Canada 36, 64, 65 and one in both the United States and Mexico 66. Three studies 37, 67, 68 were from Australia and 10 from Europe (two 69, 70 from Belgium, one each from Spain 71, 72, Finland 73, Sweden 74, the Netherlands 75, 76, one 77 in five European countries and three 35, 78, 79, 80, 81 from the United Kingdom). The populations varied across studies (some had infant populations while others were in 2.5‐ to 7‐year‐old children) and eight 35, 38, 59, 60, 61, 62, 66, 79 were in non‐representative populations.
Summary of correlates/determinants of SSB consumption
Evidence from the intervention and observational (prospective and XS) studies was pooled to identify potential determinants of young children's SSB consumption, according to levels of the socio‐ecological model (Table 3). There was little overlap between determinants targeted in intervention studies (taste exposure, knowledge, attitudes, motivation, perceived barriers, encouragement/support, skills, policy and availability) and correlates/determinants in observational studies. Of the 54 correlates/determinants, only child's age and parents' socioeconomic status and SSB consumption were determinants identified in prospective studies.
Table 3.
Correlate/determinant | Association with SSB consumption* | No. of studies | Summary[Link], [Link] | ||
---|---|---|---|---|---|
− | 0 | + | |||
Individual (child) | |||||
Sex (ref: girls) | C5, C12, C19 | C6 | 1/4 | 00 | |
Age | P3, P5, P6, C19, C23 | P4, C2, C5, C6, C22 | 5/10 | ?? | |
Knowledge | I5, I7, I12 | I2, I4, I11 | 3/6 | ?? | |
Behaviour change skills | I4 | 0/1 | 0 | ||
SSB liking/preference | C5, C6 | 2/2 | + | ||
Child milk/water consumption | C23 | P6 | C6 | 1/3 | 0 |
Child TV viewing/screen time | C5, C6, C11, C16, C17, C18, C21 | 7/7 | ++ | ||
Child snack consumption | C11 | 1/1 | + | ||
Food fussiness | C6 | 0/1 | 0 | ||
(Taste) exposure | I11 | I5 | 1/2 | ? | |
Interpersonal (parent/care giver) | |||||
Family demographics | |||||
Ethnicity (ref: white) | P6, C2, C7, C8, C20, C16 | C5, C22 | 2/8 | 00 | |
Parental age (ref: high) | C13 | C7 | C2, C20, C25, C16 | 4/6 | ++ |
Caregiver gender (ref: female) | C2, C7 | 0/2 | 0 | ||
Parents married/co‐habiting (ref: single) | C2, C20 | 2/2 | − | ||
Parent SES (ref: high) | P1, C8, C13, C25 | C2, C5, C7, C10, C19, C20, C16, C25 | 8/12 | ++ | |
Parental BMI/weight loss | I13 | P4, C12, C16 | C20 | 1/5 | 00 |
Maternal parity/N children | C2, C8, C20 | C25 | 1/4 | 00 | |
Parental psychosocial factors | |||||
Parental knowledge | I1, I5, I7, I9, I13, C4 | I2, I3, I4, I6, I11 | 6/11 | ?? | |
Parental perceived barriers | I1 | 1/1 | + | ||
Parental attitude | I10 | 1/1 | 0 | ||
Parental perception of child's diet | C21 | 0/1 | 0 | ||
Parental self‐efficacy/motivation | I6, I10, C4 | 0/3 | 0 | ||
Parental support/encouragement | I1, I9 | I3, I11, C5, C24 | 2/6 | 00 | |
Parental behaviour | |||||
Parental SSB consumption | P5, C5, C14, C24 | 4/4 | ++ | ||
Parental F&V consumption | C24F | C24V | 1/2 | ? | |
Maternal (pregnancy) smoking | C8, C25 | C10, C20 | 2/4 | ?? | |
Maternal (pregnancy) sweet consumption | C24 | C20 | 1/2 | ? | |
Parental food involvement/confidence | C2, C21 | 0/2 | 0 | ||
Parent/carer–child interaction | |||||
Parenting skills | I1, I5, I13, C4 | I3, I6, C24 | 4/7 | ?? | |
Parental (positive) modelling | I5, I13, C24 | I9, C21 | 3/5 | − − | |
Parental monitoring | C21 | C5 | 1/2 | ? | |
Formula fed (ref: breast fed) | C10, C20, C25 | 3/3 | + | ||
Early introduction to solids | P2, C8 | 2/2 | + | ||
Early introduction to SSB | P2 | 0/1 | 0 | ||
Parental rules/restriction/influence | C11, C18 | C21, C7 | C24 | 2/5 | ?? |
Pressure to eat | C24 | C21 | 1/2 | ? | |
Using food as reward | C18 | 1/1 | + | ||
Verbal/material rewards | I12 | C24 | 1/2 | ? | |
Environmental | |||||
(Pre‐)school | |||||
Attending out‐of‐home care | C6, C9 | 2/2 | + | ||
School policy | I7, I8, I12 | I2, C19 | 3/5 | − − | |
Staff knowledge | I7, I8 | I2, I11 | 2/4 | ? | |
School water availability | I2 | 0/1 | 0 | ||
Staff skills | I8 | 1/1 | − | ||
School food availability | I11 | I12 | 1/2 | ? | |
School cooking equipment | I11 | 0/1 | 0 | ||
Staff support | I7 | I11 | 1/2 | ? | |
Home | |||||
Home SSB/food availability | I12, C3, C19, C21 | I13, C5, C18 | 3/7 | ?? | |
Food security | C3, C15 | 0/2 | 0 | ||
Supermarket nearby | C6 | 1/1 | − | ||
Fast food/convenience store nearby | C6 | 1/1 | + | ||
Home location (ref: urban) | C7 | 0/1 | 0 | ||
Cost of F&V | C21 | 0/1 | 0 | ||
Community | |||||
Raising general awareness/knowledge | I7 | I2 | 1/2 | ? | |
Healthcare policy environment | I6 | 0/1 | 0 |
*Significantly lower/decreased SSB consumption; 0: no significant difference; +: significantly higher/increased SSB consumption.
**For ≤3 studies: ‘0’: 0–33% of findings support association; ‘?’ 34–59% support association; and ‘+’ or ‘−’ if 60–100% support positive or negative association.
**For ≥4 studies: ‘00’: 0–33% of findings support association; ‘??’ 34–59% support association; and ‘++’ or ‘− −’ if 60–100% support positive or negative association.
C, cross‐sectional studies – cross‐reference in Supplementary Tables 2–4; BMI, body mass index; F, fruit; V, vegetable; I, intervention studies; P, prospective studies; SES, socioeconomic status; SSB, sugar‐sweetened beverage.
Child‐level correlates/determinants
Ten individual (child)‐level correlates/determinants were investigated. There was evidence for a positive association with SSB consumption for SSB preference (2/2 studies: both XS), TV viewing/screen time (7/7 studies: all XS) and snack consumption (1/1 studies: XS). There was indeterminate evidence for child's age (5/10 studies reported a positive association: four prospective, six XS) and child's knowledge (protective effect in 3/6 intervention studies).
Interpersonal‐level correlates/determinants
Twenty‐eight interpersonal (parent/caregiver)‐level correlates/determinants were investigated. There was consistent evidence that lower parental socioeconomic (education, occupation or income) status (8/12 studies: one prospective, 11 XS), lower parental age (4/6 studies: all XS) and parental SSB consumption (4/4 studies: one prospective, three XS) were associated with higher SSB consumption in children. Parental (positive) modelling (3/5 studies: three intervention, two XS) was consistently associated with lower SSB consumption. There was some evidence that parents co‐habiting or being married (2/2: XS studies) was negatively associated, whereas parental perceived barriers (one intervention study), formula feeding (3/3: XS studies), early introduction of solids (2/2 studies: one prospective and one XS) and using food as a reward (1/1: XS study) was positively associated with SSB consumption. Parents' ethnicity, body mass index (BMI) and support or encouragement was consistently found to show no association with their child's SSB consumption.
Environmental correlates/determinants
Sixteen environmental correlates/determinants were identified. Within the preschool environment, most evidence came from four intervention studies. Together, they showed a positive influence of school policy on reduced SSB consumption (3/5 studies: four intervention, one XS). There was some evidence that attending out‐of‐home care was associated with higher SSB consumption (2/2 XS studies). The most studied correlate/determinant within the home environment was the home availability of SSB/food (in seven studies: two intervention, five XS), which showed an indeterminate/possible positive association (3/7 studies). There was some evidence from the same XS study that living near a supermarket was associated with lower consumption of SSB, whereas living near a fast food store was associated with higher SSB consumption. The influence of the healthcare policy environment was assessed in one intervention study, showing no effect on SSB consumption.
Discussion
The results of this comprehensive review show that SSB consumption in young children is influenced by factors operating at individual, interpersonal and environmental levels, consistent with the socio‐ecological theory. Most determinants of SBB consumption that were targeted in the intervention studies included in this review were not studied in the observational studies identified, and this highlights the importance of synthesizing evidence from these different types of study designs together. Overall, intervention effect was indeterminate, with six of 12 multi‐level and parental interventions showing an effect on SSB consumption in the immediate post‐intervention period.
Parental modelling and SSB consumption were consistently associated with lower SSB consumption in children, suggesting positive parental modelling should be an important component of any intervention to reduce SSB consumption in young children (and their parents). However, none of the intervention studies identified included these as targets in their interventions. Similarly, parental feeding practices (formula feeding, early introduction of solids, using food as a reward, pressure to eat, perceived barriers) also appear to be important factors to consider in designing future interventions. Lower parental socioeconomic status, age and single parenthood were associated with higher SSB consumption. These findings are consistent with other research on the topic 82, 83.
We found that child‐level correlates such as TV viewing, snack consumption and preference for SSBs were positively associated with SSB consumption. This is in agreement with the clustering of this behaviours 82, 84, although the extent to which these associations are independent of each other or due to confounding is unclear. One factor might be the potential effect of TV advertising on promoting SSB consumption among young children. Surprisingly, there was no evidence that milk/water consumption was associated with lower SSB consumption and one intervention that targeted school water availability was not effective. The correlates/determinants studied most frequently at the child level were age (10 observational studies) and knowledge (six intervention studies). Both showed indeterminate/possible evidence and may warrant further investigation.
There was a consistent association with school nutritional policies and lower SSB consumption in young children aged 0–6 years. Two XS studies showed an association between SSB consumption and attendance at day care, although this could be due to higher SSB consumption at home. Among the environmental correlates, availability of SSB at home was shown by some studies to be positively associated with SSB consumption as was concluded by another recent study in obese/overweight Latino youth 85. The role of macro‐level environmental factors such as taxation, advertising/marketing, product price and placement have yet to be studied in relation to young children's SSB consumption. However, a recent systematic review suggested that taxation may not be effective in reducing SSB consumption in adults, and the impact on children may therefore be questionable 86.
A number of correlates/determinants, including child's and caregiver's gender, number of siblings, parental support, ethnicity and BMI, were consistently not associated with SSB consumption in young children. This suggests that the effectiveness of targeting interventions based on these factors is therefore likely to be minimal.
Strengths and limitations of the review
Recent systematic reviews have highlighted the scarcity of evidence on the determinants of energy balance‐related behaviours in young children 27, 82, 83. To our knowledge, this is the first review of quantitative (interventional or observational) evidence on the determinants and correlates of SSB consumption in young children (0–6 years old).
Strict systematic review procedures were adhered to throughout the process to minimize reviewer‐related bias. No time or language restrictions were applied to ensure high sensitivity in identifying literature. Articles were hand searched and authors of included studies were contacted to identify grey and recent literature, resulting in four additional studies being included. We believe that we have included all relevant published studies (ranged from 1999 to 2014), although we are unable to rule out the possibility of publication and reporting bias 87. Also, the evidence identified came largely from economically developed countries. Our conclusions may therefore not be transferable to all countries, although increasing SSB consumption is also a public health concern in middle‐ and low‐income countries 88, 89, 90.
Conclusions
Quantitative evidence supports several potential correlates/determinants of SSB consumption in young children operating at various levels of the socio‐ecological model. Most consistent evidence for potentially modifiable correlates/determinants was for parental modelling, child's TV viewing and school policy. Interventional evidence suggests that targeting parental‐, child‐ and environmental‐level determinants together could reduce SSB consumption in the immediate post‐intervention period.
Conflict of interest statement
No conflict of interest was declared.
Author contributions
VMP (lead reviewer) and RL (overall project lead and paired reviewer) contributed to all aspects of the review. HM (initial electronic database search in August 2012) and VMP (re‐run search in June 2014) were involved in identifying evidence using predefined search strategy. CO and KH screened (and CS, EMFvS and RL double screened) a proportion of abstracts and titles. EMFvS and KKO double reviewed all intervention studies and synthesized the evidence regarding potential determinants. All authors contributed to the study design, critical revision of the manuscript and approved the final version.
Supporting information
Acknowledgements
This is an independent research funded by the National Institute of Health Research, School for Public Health Research (NIHR SPHR). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. The National Institute for Health Research's School for Public Health Research (NIHR SPHR) is a partnership between the Universities of Sheffield, Bristol, Cambridge, UCL; The London School for Hygiene and Tropical Medicine; The Peninsula College of Medicine and Dentistry; the LiLaC collaboration between the Universities of Liverpool and Lancaster and Fuse; The Centre for Translational Research in Public Health, a collaboration between Newcastle, Durham, Northumbria, Sunderland and Teesside Universities. The work was undertaken under the auspices of the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence which is funded by the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research and the Wellcome Trust (RES‐590‐28‐0002). This work was also supported by the Medical Research Council (MC_UU_12015/7).
The copyright line for this article was changed on August 27, 2015 after original online publication.
References
- 1. de Onis M, Blössner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr 2010; 92: 1257–1264. [DOI] [PubMed] [Google Scholar]
- 2. WHO . Diet, Nutrition and the Prevention of Chronic Diseases; Report of a Joint WHO/FAO Expert Consultation. World Health Organization: Geneva, 2003. [Google Scholar]
- 3. The Health and Social Care Information Centre LS . National Child Measurement Programme: England, 2012/13 School Year. [WWW document]. URL http://www.hscic.gov.uk/catalogue/PUB13115/nati‐chil‐meas‐prog‐eng‐2012‐2013‐rep.pdf (accessed December 2013).
- 4. Schmidt Morgen C, Rokholm B, Sjoberg Brixval C et al Trends in prevalence of overweight and obesity in Danish infants, children and adolescents – are we still on a plateau? PLoS ONE 2013; 8: e69860. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Rokholm B, Baker JL, Sorensen TI. The levelling off of the obesity epidemic since the year 1999 – a review of evidence and perspectives. Obes Rev 2010; 11: 835–846. [DOI] [PubMed] [Google Scholar]
- 6. Olds T, Maher C, Zumin S et al Evidence that the prevalence of childhood overweight is plateauing: data from nine countries. Int J Pediatr Obes 2011; 6: 342–360. [DOI] [PubMed] [Google Scholar]
- 7. Moss A, Klenk J, Simon K, Thaiss H, Reinehr T, Wabitsch M. Declining prevalence rates for overweight and obesity in German children starting school. Eur J Pediatr 2012; 171: 289–299. [DOI] [PubMed] [Google Scholar]
- 8. Nichols MS, Silva‐Sanigorski A, Cleary JE, Goldfeld SR, Colahan A, Swinburn BA. Decreasing trends in overweight and obesity among an Australian population of preschool children. Int J Obes (Lond) 2011; 35: 916–924. [DOI] [PubMed] [Google Scholar]
- 9. Morgen CS, Sorensen TI. Obesity: global trends in the prevalence of overweight and obesity. Nat Rev Endocrinol 2014; 10: 513–514. [DOI] [PubMed] [Google Scholar]
- 10. Cunningham SA, Kramer MR, Narayan KMV. Incidence of childhood obesity in the United States. NEJM 2014; 370: 403–411. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. DeBoer MD, Scharf RJ, Demmer RT. Sugar‐sweetened beverages and weight gain in 2‐ to 5‐year‐old children. Pediatrics 2013; 132: 413–420. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Papandreou D, Andreou E, Heraclides A, Rousso I. Is beverage intake related to overweight and obesity in school children? Hippokratia 2013; 17: 42–46. [PMC free article] [PubMed] [Google Scholar]
- 13. Cantoral A, Tellez‐Rojo MM, Ettinger AS, Hu H, Hernandez‐Avila M, Peterson K. Early introduction and cumulative consumption of sugar‐sweetened beverages during the pre‐school period and risk of obesity at 8–14 years of age. Pediatr Obes 2015. doi: 10.1111/ijpo.12023. [Epub ahead of print]. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Pan L, Li R, Park S, Galuska DA, Sherry B, Freedman DS. A longitudinal analysis of sugar‐sweetened beverage intake in infancy and obesity at 6 years. Pediatrics 2014; 134(Suppl. 1): S29–S35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Hu FB. Resolved: there is sufficient scientific evidence that decreasing sugar‐sweetened beverage consumption will reduce the prevalence of obesity and obesity‐related diseases. Obes Rev 2013; 14: 606–619. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Malik VS, Pan A, Willett WC, Hu FB. Sugar‐sweetened beverages and weight gain in children and adults: a systematic review and meta‐analysis. Am J Clin Nutr 2013; 98: 1084–1102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Mathias KC, Slining MM, Popkin BM. Foods and beverages associated with higher intake of sugar‐sweetened beverages. Am J Prev Med 2013; 44: 351–357. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Marshall TA. Preventing dental caries associated with sugar‐sweetened beverages. J Am Dent Assoc 2013; 144: 1148–1152. [DOI] [PubMed] [Google Scholar]
- 19. Catteau C, Trentesaux T, Delfosse C, Rousset MM. Consumption of fruit juices and fruit drinks: impact on the health of children and teenagers, the dentist's point of view. Arch Pediatr 2012; 19: 118–124. [DOI] [PubMed] [Google Scholar]
- 20. Kosova EC, Auinger P, Bremer AA. The relationships between sugar‐sweetened beverage intake and cardiometabolic markers in young children. J Acad Nutr Diet 2013; 113: 219–227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Ambrosini GL, Oddy WH, Huang RC, Mori TA, Beilin LJ, Jebb SA. Prospective associations between sugar‐sweetened beverage intakes and cardiometabolic risk factors in adolescents. Am J Clin Nutr 2013; 98: 327–334. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Brazionis L, Golley RK, Mittinty MN et al Diet spanning infancy and toddlerhood is associated with child blood pressure at age 7.5 y. Am J Clin Nutr 2013; 97: 1375–1386. [DOI] [PubMed] [Google Scholar]
- 23. Lakshman R, Elks CE, Ong KK. Childhood obesity. Circulation 2012; 126: 1770–1779. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Malik VS, Schulze MB, Hu FB. Intake of sugar‐sweetened beverages and weight gain: a systematic review. Am J Clin Nutr 2006; 84: 274–288. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Kaiser KA, Shikany JM, Keating KD, Allison DB. Will reducing sugar‐sweetened beverage consumption reduce obesity? Evidence supporting conjecture is strong, but evidence when testing effect is weak. Obes Rev 2013; 14: 620–633. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Kuhl ES, Clifford LM, Stark LJ. Obesity in preschoolers: behavioral correlates and directions for treatment. Obesity 2012; 20: 3–29. [DOI] [PubMed] [Google Scholar]
- 27. Hesketh KD, Campbell KJ. Interventions to prevent obesity in 0–5 year olds: an updated systematic review of the literature. Obesity (Silver Spring) 2010; 18(S1): S27–S35. [DOI] [PubMed] [Google Scholar]
- 28. Nixon CA, Moore HJ, Douthwaite W et al Identifying effective behavioural models and behaviour change strategies underpinning preschool‐ and school‐based obesity prevention interventions aimed at 4–6‐year‐olds: a systematic review. Obes Rev 2012; 13: 106–117. [DOI] [PubMed] [Google Scholar]
- 29. Millen B, Lichtenstein A, Abrams S et al Scientific Report of the 2015 Dietary Guidelines Advisory Committee 2015: 1–571. [WWW document]. URL http://www.health.gov/dietaryguidelines/2015‐scientific‐report/ (accessed July 2015).
- 30. Johnson RK, Appel LJ, Brands M et al Dietary sugars intake and cardiovascular health: a scientific statement from the American Heart Association. Circulation 2009; 120: 1011–1020. [DOI] [PubMed] [Google Scholar]
- 31. Lakshman R, Mazarello Paes V, Hesketh K et al Protocol for systematic reviews of determinants/correlates of obesity‐related dietary and physical activity behaviors in young children (preschool 0 to 6 years): evidence mapping and syntheses. Syst Rev 2013; 2: 28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Evidence for Policy and Practice Information (EPPI) and Co‐ordinating Centre IoE, University of London . EPPI – Centre Methods for Conducting Systematic Reviews. [WWW document]. URL http://eppi.ioe.ac.uk/cms/ (accessed July 2015).
- 33. Campbell KJ, Lioret S, McNaughton SA et al A parent‐focused intervention to reduce infant obesity risk behaviors: a randomized trial. Pediatrics 2013; 131: 652–660. [DOI] [PubMed] [Google Scholar]
- 34. De Coen V, De Bourdeaudhuij I, Vereecken C et al Effects of a 2‐year healthy eating and physical activity intervention for 3–6‐year‐olds in communities of high and low socio‐economic status: the POP (Prevention of Overweight among Pre‐school and school children) project. Public Health Nutr 2012; 15: 1737–1745. [DOI] [PubMed] [Google Scholar]
- 35. Ohly H, Pealing J, Hayter AKM et al Parental food involvement predicts parent and child intakes of fruits and vegetables. Appetite 2013; 69: 8–14. [DOI] [PubMed] [Google Scholar]
- 36. Pabayo R, Spence JC, Cutumisu N, Casey L, Storey K. Sociodemographic, behavioural and environmental correlates of sweetened beverage consumption among pre‐school children. Public Health Nutr 2012; 15: 1338–1346. [DOI] [PubMed] [Google Scholar]
- 37. Koh GA, Scott JA, Oddy WH, Graham KI, Binns CW. Exposure to non‐core foods and beverages in the first year of life: results from a cohort study. Nutr Diet 2010; 67: 137–142. [Google Scholar]
- 38. Lim S, Zoellner JM, Lee JM et al Obesity and sugar‐sweetened beverages in African‐American preschool children: a longitudinal study. Obesity (Silver Spring) 2009; 17: 1262–1268. [DOI] [PubMed] [Google Scholar]
- 39. Hinkley T, Crawford D, Salmon J, Okely AD, Hesketh K. Preschool children and physical activity: a review of correlates. Am J Prev Med 2008; 34: 435–441. [DOI] [PubMed] [Google Scholar]
- 40. Sallis JF, Prochaska JJ, Taylor WC. A review of correlates of physical activity of children and adolescents. Med Sci Sports Exerc 2000; 32: 963–975. [DOI] [PubMed] [Google Scholar]
- 41. Llargues E, Franco R, Recasens A et al Assessment of a school‐based intervention in eating habits and physical activity in school children: the AVall study. J Epidemiol Community Health 2011; 65: 896–901. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Vereecken C, Huybrechts I, van Houte H, Martens V, Wittebroodt I, Maes L. Results from a dietary intervention study in preschools ‘Beastly Healthy at School’. Int J Public Health 2009; 54: 142–149. [DOI] [PubMed] [Google Scholar]
- 43. de Silva‐Sanigorski AM, Bell AC, Kremer P et al Reducing obesity in early childhood: results from Romp & Chomp, an Australian community‐wide intervention program. Am J Clin Nutr 2010; 91: 831–840. [DOI] [PubMed] [Google Scholar]
- 44. Korwanich K, Sheiham A, Srisuphan W, Srisilapanan P. Promoting healthy eating in nursery schoolchildren: a quasi‐experimental intervention study. Health Educ J 2008; 67: 16–30. [Google Scholar]
- 45. Taveras EM, Gortmaker SL, Hohman KH et al Randomized controlled trial to improve primary care to prevent and manage childhood obesity: the High Five for Kids study. Arch Pediatr Adolesc Med 2011; 165: 714–722. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Stark LJ, Spear S, Boles R et al A pilot randomized controlled trial of a clinic and home‐based behavioral intervention to decrease obesity in preschoolers. Obesity 2011; 19: 134–141. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Klohe‐Lehman DM, Freeland‐Graves J, Clarke KK et al Low‐income, overweight and obese mothers as agents of change to improve food choices, fat habits, and physical activity in their 1‐to‐3‐year‐old children. J Am Coll Nutr 2007; 26: 196–208. [DOI] [PubMed] [Google Scholar]
- 48. Watt RG, Tull KI, Hardy R et al Effectiveness of a social support intervention on infant feeding practices: randomised controlled trial. J Epidemiol Community Health 2009; 63: 156–162. [DOI] [PubMed] [Google Scholar]
- 49. Scheiwe A, Hardy R, Watt RG. Four‐year follow‐up of a randomized controlled trial of a social support intervention on infant feeding practices. Matern Child Nutr 2010; 6: 328–337. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50. Jones R, Wells M, Okely A, Lockyer L, Walton K. Is an online healthy lifestyles program acceptable for parents of preschool children? Nutr Diet 2011; 68: 149–154. [Google Scholar]
- 51. Whaley SE, McGregor S, Jiang L, Gomez J, Harrison G, Jenks E. A WIC‐based intervention to prevent early childhood overweight. J Nutr Educ Behav 2010; 42(3 Suppl. ): S47–S51. [DOI] [PubMed] [Google Scholar]
- 52. Hardy LL, King L, Kelly B, Farrell L, Howlett S. Munch and Move: evaluation of a preschool healthy eating and movement skill program. Int J Behav Nutr Phys Act 2010; 7: 80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53. Hoerr SL, Lee SY, Schiffman RF, Horodynski MO, McKelvey L. Beverage consumption of mother‐toddler dyads in families with limited incomes. J Pediatr Nurs 2006; 21: 403–411. [DOI] [PubMed] [Google Scholar]
- 54. Kral TVE, Stunkard AJ, Berkowitz RI, Stallings VA, Moore RH, Faith MS. Beverage consumption patterns of children born at different risk of obesity. Obesity (Silver Spring) 2008; 16: 1802–1808. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55. Alexy U, Sichert‐Hellert W, Kersting M, Manz F, Schoch G. Fruit juice consumption and the prevalence of obesity and short stature in German preschool children: results of the DONALD Study. Dortmund Nutritional and Anthropometrical Longitudinally Designed. J Pediatr Gastroenterol Nutr 1999; 29: 343–349. [DOI] [PubMed] [Google Scholar]
- 56. Erinosho TO, Berrigan D, Thompson FE, Moser RP, Nebeling LC, Yaroch AL. Dietary intake of preschool‐aged children in relation to caregivers' race/ethnicity, demographic characteristics, and acculturation: the 2007 California Health Interview Survey. FASEB J 2011; 16: 931 DOI: 10.1007/s10995‐011‐0931‐5. [DOI] [PubMed] [Google Scholar]
- 57. Marshall TA, Eichenberger Gilmore JM, Broffitt B, Stumbo PJ, Levy SM. Diet quality in young children is influenced by beverage consumption. J Am Coll Nutr 2005; 24: 65–75. [DOI] [PubMed] [Google Scholar]
- 58. Miller SA, Taveras EM, Rifas‐Shiman SL, Gillman MW. Association between television viewing and poor diet quality in young children. Int J Pediatr Obes 2008; 3: 168–176. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59. Papas MA, Hurley KM, Quigg AM, Oberlander SE, Black MM. Low‐income, African American adolescent mothers and their toddlers exhibit similar dietary variety patterns. J Nutr Educ Behav 2009; 41: 87–94. [DOI] [PubMed] [Google Scholar]
- 60. Bauer KW, Widome R, Himes JH et al High food insecurity and its correlates among families living on a rural American Indian reservation. Am J Public Health 2012; 102: 1346–1352. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61. Kong A, Odoms‐Young AM, Schiffer LA et al Racial/ethnic differences in dietary intake among WIC families prior to food package revisions. J Nutr Educ Behav 2013; 45: 39–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62. Goodell LS, Pierce MB, Amico KR, Ferris AM. Parental information, motivation, and behavioral skills correlate with child sweetened beverage consumption. J Nutr Educ Behav 2012; 44: 240–245. [DOI] [PubMed] [Google Scholar]
- 63. Mennella JA, Ziegler P, Briefel R, Novak T. Feeding Infants and Toddlers Study: the types of foods fed to Hispanic infants and toddlers. J Am Diet Assoc 2006; 106(1 Suppl. 1): S96–S106. [DOI] [PubMed] [Google Scholar]
- 64. Dubois L, Farmer A, Girard M, Peterson K. Regular sugar‐sweetened beverage consumption between meals increases risk of overweight among preschool‐aged children. J Am Diet Assoc 2007; 107: 924–934. [DOI] [PubMed] [Google Scholar]
- 65. Dubois L, Farmer A, Girard M, Peterson K. Social factors and television use during meals and snacks is associated with higher BMI among pre‐school children. Public Health Nutr 2008; 11: 1267–1279. [DOI] [PubMed] [Google Scholar]
- 66. Rosas LG, Harley K, Fernald LCH et al Dietary associations of household food insecurity among children of Mexican descent: results of a binational study. J Am Diet Assoc 2009; 109: 2001–2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67. Spurrier NJ, Magarey AA, Golley R, Curnow F, Sawyer MG. Relationships between the home environment and physical activity and dietary patterns of preschool children: a cross‐sectional study. Int J Behav Nutr Phys Act 2008; 5: 31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68. Campbell KJ, Crawford DA, Ball K. Family food environment and dietary behaviors likely to promote fatness in 5–6 year‐old children. Int J Obes (Lond) 2006; 30: 1272–1280. [DOI] [PubMed] [Google Scholar]
- 69. Vereecken CA, Keukelier E, Maes L. Influence of mother's educational level on food parenting practices and food habits of young children. Appetite 2004; 43: 93–103. [DOI] [PubMed] [Google Scholar]
- 70. Vereecken C, Huybrechts I, Maes L, De Henauw S. Food consumption among preschoolers. Does the school make a difference? Appetite 2008; 51: 723–726. [DOI] [PubMed] [Google Scholar]
- 71. Navia B, Ortega RM, Requejo AM, Perea JM, Lopez‐Sobaler AM, Faci M. Influence of maternal education on food consumption and energy and nutrient intake in a group of pre‐school children from Madrid. Int J Vitam Nutr Res 2003; 73: 439–445. [DOI] [PubMed] [Google Scholar]
- 72. Navia B, Ortega RM, Rodriguez‐Rodriguez E, Aparicio A, Perea JM. Mother's age as a conditioning factor of food consumption and energy and nutrients intake of their offspring at pre‐school age. Nutr Hosp 2009; 24: 452–458. [PubMed] [Google Scholar]
- 73. Lehtisalo J, Erkkola M, Tapanainen H et al Food consumption and nutrient intake in day care and at home in 3‐year‐old Finnish children. Public Health Nutr 2010; 13(6A): 957–964. [DOI] [PubMed] [Google Scholar]
- 74. Brekke HK, van Odijk J, Ludvigsson J. Predictors and dietary consequences of frequent intake of high‐sugar, low‐nutrient foods in 1‐year‐old children participating in the ABIS study. Br J Nutr 2007; 97: 176–181. [DOI] [PubMed] [Google Scholar]
- 75. Gubbels JS, Kremers SPJ, Stafleu A et al Clustering of dietary intake and sedentary behavior in 2‐year‐old children. J Pediatr 2009; 155: 194–198. [DOI] [PubMed] [Google Scholar]
- 76. Gubbels JS, Kremers SPJ, Stafleu A et al Diet‐related restrictive parenting practices. Impact on dietary intake of 2‐year‐old children and interactions with child characteristics. Appetite 2009; 52: 423–429. [DOI] [PubMed] [Google Scholar]
- 77. Schiess SA, Grote V, Scaglioni S et al Intake of energy providing liquids during the first year of life in five European countries. Clin Nutr 2010; 29: 726–732. [DOI] [PubMed] [Google Scholar]
- 78. McGowan L, Croker H, Wardle J, Cooke LJ. Environmental and individual determinants of core and non‐core food and drink intake in preschool‐aged children in the United Kingdom. Eur J Clin Nutr 2012; 66: 322–328. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79. Johnson L, Mander AP, Jones LR, Emmett PM, Jebb SA. Is sugar‐sweetened beverage consumption associated with increased fatness in children? Nutrition 2007; 23: 557–563. [DOI] [PubMed] [Google Scholar]
- 80. Rogers I, Emmett P, Team AS. The effect of maternal smoking status, educational level and age on food and nutrient intakes in preschool children: results from the Avon Longitudinal Study of Parents and Children. Eur J Clin Nutr 2003; 57: 854–864. [DOI] [PubMed] [Google Scholar]
- 81. Northstone K, Rogers I, Emmett P. Drinks consumed by 18‐month‐old children: are current recommendations being followed? Eur J Clin Nutr 2002; 56: 236–244. [DOI] [PubMed] [Google Scholar]
- 82. De Craemer M, De Decker E, De Bourdeaudhuij I et al Correlates of energy balance‐related behaviours in preschool children: a systematic review. Obes Rev 2012; 13: 13–28. [DOI] [PubMed] [Google Scholar]
- 83. te Velde SJ, van Nassau F, Uijtdewilligen L et al Energy balance‐related behaviours associated with overweight and obesity in preschool children: a systematic review of prospective studies. Obes Rev 2012; 13: 56–74. [DOI] [PubMed] [Google Scholar]
- 84. Olafsdottir S, Berg C, Eiben G et al Young children's screen activities, sweet drink consumption and anthropometry: results from a prospective European study. Eur J Clin Nutr 2014; 68: 223–228. [DOI] [PubMed] [Google Scholar]
- 85. Bogart LM, Cowgill BO, Sharma AJ et al Parental and home environmental facilitators of sugar‐sweetened beverage consumption among overweight and obese Latino youth. Acad Pediatr 2013; 13: 348–355. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86. Maniadakis N, Kapaki V, Damianidi L, Kourlaba G. A systematic review of the effectiveness of taxes on nonalcoholic beverages and high‐in‐fat foods as a means to prevent obesity trends. Clinicoecon Outcomes Res 2013; 5: 519–543. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87. Bes‐Rastrollo M, Schulze MB, Ruiz‐Canela M, Martinez‐Gonzalez MA. Financial conflicts of interest and reporting bias regarding the association between sugar‐sweetened beverages and weight gain: a systematic review of systematic reviews. PLoS Med 2013; 10: 31 DOI:10.1371/journal.pmed.1001578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88. Barquera S, Campirano F, Bonvecchio A, Hernandez‐Barrera L, Rivera JA, Popkin BM. Caloric beverage consumption patterns in Mexican children. Nutr J 2010; 9: 47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 89. Pan WH, Wu HJ, Yeh CJ et al Diet and health trends in Taiwan: comparison of two nutrition and health surveys from 1993–1996 and 2005–2008. Asia Pac J Clin Nutr 2011; 20: 238–250. [PubMed] [Google Scholar]
- 90. Albala C, Vio F, Kain J, Uauy R. Nutrition transition in Chile: determinants and consequences. Public Health Nutr 2002; 5(1a): 123–128. [DOI] [PubMed] [Google Scholar]
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