Purpose:  In Australia, about 50% of children aged 2-5 years spend significant hours per week in early childhood education and care (ECEC), whilst almost all children aged 6-11years spend five days a week in primary schools.  These settings are therefore important food environments to influence children’s food intake, however there is heterogeneity in the tools used to measure food provision in these settings.  Our systematic review aimed to identify, evaluate, and make recommendations for the standardisation of service level food provision measurement tools in ECEC and primary schools. 

Methods:   This review followed the Preferred Reporting Items for Systematic Reviews (PRISMA). Included studies were those published in English between 2003-2020 that implemented, validated or developed service level food provision measurement tools within ECEC or primary school.  Two reviewers undertook data screening, critical appraisal and extraction, with cross checking by a third reviewer and verified by all authors.  The Academy of Nutrition and Dietetics Quality Criteria Checklist (QCC) was used to critically appraise each study. 

Results/findings: Eighty-one eligible studies were included – 46 in ECEC and 35 in primary schools.  Seven food provision measurement tools were identified, namely: 1) Menu review; 2) Observation; 3) Weighed food protocol; 4) Questionnaire/survey; 5) Digital photography 6) Quick menu audit and 7) Web-based menu assessment.  An evidence-based evaluation was conducted for each tool.  The weighed food protocol was found to be the most popular and accurate measurement tool however future research is recommended to validate this tool for service level measurement.  The quick menu audit was a validated and cost-effective tool used in primary school settings where food was available for purchase from a canteen.  The web-based menu assessment tool showed promise as a self-report measurement tool in ECEC and primary schools.      

Conclusions: A weighed food protocol, validated for service level food provision, appears to offer a standardised method for food provision measurement within ECEC and primary school settings.  The potential use of a web-based menu assessment tool as a self-report measurement tool should also be explored.  

Purpose: The interaction of physical activity (PA) and sedentary behavior (SB) may differ according to sex and may play an important role on health-related outcomes. This systematic review (Prospero CRD42018094826) aimed to identify clusters of PA and SB among boys and girls.

Method: An electronic database search was performed PubMed, Web of Science, LILACS, Scopus, PsycINFO. Eligibility criteria were: (1) studies with children and/or adolescents (aged 0–19 years); (2) analyzed PA, and SB by applying data-based cluster procedures. Cluster characteristics were extracted in accordance to authors’ descriptions. All the screening process and synthesis of data and methodological quality of the studies were made by two independent reviewers and a third reviewer was consulted for the consensus of disagreements. 

Results: Searches identified a total of 11,910 articles, which 18 were eligible. Ten and twelve cluster types were identified to girls and boys, respectively. Most girls were in cluster characterized by “Low PA High/Low SB”, follow by “Low PA Low SB” and “Low PA High SB”. However, most of boys were at clusters types “High PA Low SB”, follow by “High PA High SB” and “High PA moderate SB”. Girls have been allocated in profiles with lower levels of PA with high time in SB related to socializing components (e.g., siting talking to a friend) compared to boys. On the other hand, boys have been in clusters characterized by high levels of PA and SB related to using computer and playing videogame.

Conclusion: Clusters between girls and boys differ considering PA levels and SB components. Our results support the need of intervention programs targeting more than one behavior at the same time and suggest that should be considered strategies to the type of screen rather than only use-time.

Purpose: Childcare settings play an important role in shaping young children’s eating behaviors. However, less research has focused on family childcare homes (FCCHs) than centers. This analysis examines whether 2-5-year-old children that are cared for in FCCHs have better diet quality if their provider adheres to best practices for nutrition.

Methods: We analyzed baseline two-day observation data collected using the Environment and Policy Assessment and Observation measure from a cluster-randomized trial. Following the observation, 26 nutrition best practices were dichotomized into met vs. not met, based on the Nutrition and Physical Activity Self-Assessment for Child Care. Multilevel linear regression models assessed the association between providers meeting nutrition best practices and children’s diet quality (Healthy Eating Index (HEI-2015 total and subscores). Each model was clustered by FCCH, controlling for provider ethnicity and income level.

Results: FCCH providers (n=120) were all female, with over half identifying as Latinx (67.5%). Participating children (n=370) were 51% female, 58% Latinx. We found that a higher overall nutrition best practices score was associated with higher child diet quality (ß=1.05, 95%CI =[0.12, 1.99], p<.05). Providers who met the following two best practices had children with higher total HEI scores compared to those who did not meet these practices: allowing/teaching children to serve themselves (ß =27.52, 95%CI =[21.02, 34.02], p<.001); and talking with children informally about nutrition (ß =7.76, 95%CI =[3.29, 12.23], p<.001). In addition, meeting best practices related to serving certain foods or beverages was associated with higher related HEI sub-scores for the following: total fruit (ß =.59, 95%CI =[.05, 1.13], p<.05), whole fruit (ß =.73, 95%CI =[.20, 1.26], p<.01), whole grain (ß =2.47, 95%CI =[.52, 4.42], p<.05), refined grain (ß =2.47 95%CI =[.45, 6.02], p<.01), and added sugar (ß =3.24, 95%CI =[.45,6.02], p<.05).

Conclusion: Overall, children cared for in FCCHs where providers met certain nutrition best practices had better diet quality. Future interventions and policies should help to support FCCH providers in meeting nutrition best practices to achieve better nutrition environments in FCCHs and to improve children’s diet quality.

Purpose: School-based health-promoting interventions are increasingly seen as an effective population strategy to improve health and prevent obesity. Evidence on the long-term effectiveness of school-based interventions is scarce. This study investigates the four-year effectiveness of the school-based Healthy Primary School of the Future (HPSF) intervention on children’s body mass index z-score (BMIz), and on secondary outcomes waist circumference (WC), and dietary and physical activity (PA) behaviours.

Methods: This study has a quasi-experimental design with four intervention schools, i.e., two full HPSFs (focus: diet and PA), two partial HPSFs (focus: PA), and four control schools. Children (aged 4-12 years) attending the eight participating schools were invited to enrol in the study between 2015 and 2019, the study had an open cohort. Annual measurements consisted of children’s anthropometry (weight, height, and WC), dietary behaviours (e.g., lunch intake, fruit, vegetable, and water consumption) and PA levels (accelerometers). Two-level linear mixed-model analyses were used to analyse intervention effects on continuous outcome measures, General Estimating Equations (GEE) were used for the binary outcome variable (lunch intake).

Findings: Between 2015 and 2019, 2236 children enrolled. The average exposure to the school condition was 2.66 (SD 1.33) years, 900 participants were exposed for the full four years (40.3%). After four years of intervention, both full (estimated intervention effect B=-0.17, p=0.000) and partial HPSF (B=-0.16, p=0.001) resulted in significant changes in children’s BMIz compared to control schools. BMIz in full and partial HPSFs remained unchanged, whereas BMIz in control schools increased significantly over time. WC changed in favour of both full (B=-1.46, p=0.001) and partial HPSFs (B=-1.56, p=0.000). In full HPSFs, almost all dietary behaviours changed significantly in the short term compared to control schools. In the long term, only consumption of water (B=0.54, p=0.000) and dairy (Odds Ratio=1.65, p=0.037) remained significant compared to control schools. In both full and partial HPSFs, changes in PA behaviours were mostly absent.

Conclusions: This school-based health-promoting intervention is effective in bringing unfavourable changes in body composition to a halt in both the short and long term. It provides policy makers with robust evidence to sustainably implement these interventions in school-based routine.

Methods: Twelve Head Start centers were randomly assigned to one of three treatment arms: center-based intervention (CBI), center-based plus home-based intervention (CBI+HBI), or control. Parents of three-year-old children completed questionnaires before and after the 8-month intervention on child’s EBRBs at home. Adult-facilitated physical activity (PA) was measured by an index based on the level of the children’s participation in PA at home with an adult or facilitated by an adult. Fruit, vegetable, and added sugar intake were measured by food frequency questionnaire, and sleep and screen time were measured by seven-day logs. Linear mixed effects model was used to examine the effect of each treatment (CBI and CBI+HBI) compared to control on changes in PA, intake of fruit, vegetable, and added sugar, sleep and screen time from baseline to posttest. 

Results: A total of 325 surveys were completed by parents (n=101 CBI, n=101 CBI+HBI, and n=123 control) at baseline. Children from CBI+HBI reported increased adult-facilitated PA (+0.44, SE=0.12, p<0.05) and fruit and vegetable intake (+0.67 cups, SE=0.31, p<0.05) and both CBI (-0.67 tsp, SE=0.31, p<0.05) and CBI+HBI (-0.60 tsp, SE=0.29, p<0.05) groups reported decreases in added sugar from sugar sweetened beverages at home. CBI (+0.16 hrs, SE=0.09, p=0.006) and CBI+HBI (+0.02 hrs, SE=0.09, p<0.001) groups had lower increases in children’s average weekday screen time as compared to control. Children in the CBI+HBI had increased their daily sleep time during weekdays (+0.28 hrs, SE=0.09, p=0.04) and the week (+0.25 hrs, SE=0.10, p=0.009), while children in the CBI increased sleep time over the week (+0.10 hrs, SE=09, p=0.03) compared to the children in the control group.

Conclusions: Improvement in EBRBs can be strengthened by modifications of childcare policies and staff practice, enhancement of PA and health education programs, and parental engagement. Future studies should investigate equity-related contextual factors that influence the impact of obesity prevention in health-disparity populations.