Examining Changes in Implementation of Priority Healthy Eating and Physical Activity Practices,and Related Barriers,Over Time in Australian Early Childhood Education and Care Services: A Repeated Cross-Sectional Study

Abstract

Background:

Promoting healthy eating and physical activity in early childhood education and care (ECEC) is recommended within guidelines and supported by health promotion programs; however, implementation is suboptimal. Evidence suggests implementation within the sector varies over time; however, this has not been empirically examined in relation to implementation barriers. This study aims to: (1) describe changes in the prevalence of, and barriers to, implementation of priority healthy eating and physical activity practices; and (2) explore the associations between such barriers and implementation.

Methods:

This was a repeated cross-sectional study over an 8-month period. A cross-section of 150–180 Australian ECEC services were prospectively randomly sampled for each month (April–November 2023), with 1127 ECEC services sampled in total and 20% of services sampled twice. Services reported via survey their implementation of two priority practices: (1) healthy menu standards and (2) educating and engaging parents in child physical activity. They also reported on implementation status, implementation stage, and five core implementation barriers.

Results:

Overall, 716 services completed 809 surveys. There were no significant differences in the prevalence of implementation or general trends in barriers to implementation of the two priority practices across that time. Services reporting less barriers were significantly more likely to be implementing the priority practices, and services in more advanced implementation stages were significantly less likely to report barriers.

Conclusions:

To enhance the implementation of priority practices in ECEC services, key barriers to implementation need to be understood and targeted to progress services through to advanced implementation stages.

Background

Overweight and obesity in early childhood are associated with premature mortality and increased risk of chronic disease.¹ Interventions delivered in early childhood education and care (ECEC) services are recommended public health strategies to help reduce dietary risk factors and physical inactivity that contribute to obesity.^2–4 ECEC settings provide broad reach to young children, aged birth to approximately 6 years, during a critical development period where lifestyle behaviors are established.⁵ International, national, and state guidelines recommend that ECEC services deliver programs to support children’s healthy eating and movement behaviors.^6–9

Systematic reviews of ECEC-based interventions have identified practices that improve child diet and physical activity behaviors.^10,11 In Australia, such strategies are supported by national accreditation standards for the sector,⁶ state guidelines,⁷ and key stakeholders.¹² However, there continues to be varied and suboptimal implementation at a population level.^13–15 A nationally representative survey of 1028 ECEC services found that implementation of healthy eating and physical activity practices, including multicomponent practices, varied from 14% to 95% depending on the practice.¹⁴

To better support population-level implementation, it is well established that strategies are tailored to address end-user-reported barriers.^16–18 Previous studies of large-scale nutrition and physical activity implementation programs attempted to identify barriers at a single time point, typically prior to initiation of implementation efforts.¹⁹ Researchers describe the dynamic nature of implementation and sustainability, highlighting the need for ongoing refinements of implementation strategies to address changing contexts/barriers, ensuring sustained intervention impact.^20,21

Evidence in ECEC suggests that barriers to program implementation change over time according to the implementation stage, likely necessitating a change in support strategies. A longitudinal study found that barriers to integrating early childhood screening shifted throughout the implementation process from pre-implementation through to sustained implementation.²² It is unknown how long it takes to transition through implementation stages without active intervention. Systematic review evidence suggests that changes in implementation can occur after just a few months of support for ECEC services,¹⁸ with one study reporting improved intentions to implement healthy menu planning guidelines within less than 2 months of receiving a printed educational resource.²³ Implementation of population health programs can also be impacted by contextual factors including short-term political and funding cycles,²⁴ suggesting a need to regularly monitor sector-level changes in the prevalence of implementation. A better understanding of how ECEC-reported barriers to the implementation of healthy eating and physical activity programs change throughout the implementation process may help ensure that ongoing support can address these barriers as they arise.

Limited studies have described population-level changes in the implementation of healthy eating and physical activity interventions in ECEC services over time^25,26; however, this has not been explored in relation to how barriers change. In a repeated cross-sectional survey of Australian ECEC services across a period of 8 months, the aim of this study was to: (1) describe changes in the prevalence of, and barriers to, implementation of priority healthy eating and physical activity practices; and (2) explore the associations between such barriers and implementation.

Methods

Design

This study utilized a repeated cross-sectional study design. This involved repeated cross-sectional surveys of different samples from the same population, conducted monthly over an 8-month period (April–November 2023).

Context

This study is a descriptive study with no active intervention. Nationally, there are programs that ECECs can access to support the implementation of healthy eating and physical activity practices, such as the Munch & Move program in New South Wales (NSW) and the Achievement Program in Victoria. We hypothesized that such programs may influence implementation, although we were unaware of the specific program activities during this time. As this study was exploratory rather than evaluative in nature, the impact of specific programs and any sector-level changes over the study period could not be assessed.

Participants

Australian ECEC services, including long day care services and preschools, were identified for the researchers’ 2021 survey, via the publicly available Australian Children’s Education & Care Quality Authority (ACECQA) register.²⁷ Services from 2021 that had not declined being recontacted (N = 1195) were included in a panel of services. An additional 196 services from the Hunter New England Local Health District (HNELHD) area were included based on the priorities of research partners. A total of 1391 services, with an overrepresentation of NSW and HNELHD services, formed the eligible sample for this study. The overrepresentation was intentional, as the study offered valuable insights to state and local early childhood health promotion teams, helping to identify areas for future support.

Nominated supervisors were asked to respond on behalf of their ECEC services, as they are responsible for daily service management²⁸ and are legally responsible for ensuring their service’s educational programs are designed and implemented in line with the National Quality Framework, which includes elements related to healthy eating and physical activity.⁶

Procedure

Randomization was undertaken by an independent statistician using SAS software v9.4 (SAS Institute Inc., Cary, NC). A cross-section of 150 services (180 from month 4) was prospectively randomly sampled for each month, with a total of 1127 ECEC services randomly sampled over the 8-month study period. Approximately 20% of services were sampled twice for a cohort sample.

The online survey link was emailed to ECEC services. A reminder email was sent 2 days later, followed by phone call reminders 1 week after the initial email. The survey was hosted within REDCap, a secure web application.^29,30

Ethics approval was granted by the Hunter New England Human Research Ethics Committee (2019/ETH12353) and ratified by the University of Newcastle and Deakin University Human Research Ethics Committees (H-2008-0343; 2023-062). Survey completion was taken as implied consent to participate.

Measures

Implementation of priority practices

Two multicomponent practices: (1) meeting healthy menu standards for ECEC and (2) educating and engaging families in their children’s physical activity, were deemed priorities based on the following evidence-based and consultative processes. A mapping process identified evidence-based components of ECEC-based healthy eating and physical activity interventions.^7,10,11 Additionally, the practices were previously reported to have low implementation,¹⁴ considered priorities by key stakeholders,¹² targets of state health promotion programs, consistent with national accreditation standards, and approved by an expert advisory group.

Practices were assessed with items previously used and adapted from validated items.^31–33 Supplementary Data S1 contains the definitions of the two priority practices and survey items used to assess their implementation. To determine implementation status, services were dichotomized into “implementing” and “not implementing” based on the implementation criteria listed in Supplementary Data S1.

ECEC services’ perceived implementation stage for each priority practice was measured by adapting a previously developed item.^34,35 The five stages (i.e., precontemplation, contemplation, preparation, action, and maintenance) represent a temporal sequence of change in relation to behavior, such as stages of change in implementation of evidence-based practices.³⁵ Supplementary Data S1 contains the survey items with response options that corresponded to the five implementation stages.

Barriers to implementing priority practices

The assessment of barriers was informed by the Theoretical Domains Framework (TDF), which includes 14 domains, as it has been extensively used to assess implementation determinants.³⁶ To reduce participant burden, selected domains were identified from previous systematic reviews,^37–39 and informed by the research team’s 2021 survey. The top five domains included staff knowledge, staff skills, staff beliefs about capabilities, ECEC environmental context and resources, and social influences. An adapted version of validated survey items was used to assess the relevant TDF domains.^39,40 Supplementary Data S2 contains details of the TDF domains, definitions, and survey items.

The items were positively worded and scored on a 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7). For each TDF domain (see Supplementary Data S2), a median score was created from all items within the domain, similar to previous research using measures of central tendency for TDF domain scores.^40,41 Higher scores indicated that the domain was considered to be less of a barrier.

Service characteristics

The survey assessed the number of children enrolled (i.e., service size) and service type (i.e., long day care or preschool). State and territory locations were obtained from public information available from the national register of ECEC services (i.e., ACECQA).

Statistical Analysis

All analyses were performed using R Statistical Software version 4.3.1,⁴² setting significance at 5% (α = 0.05). Descriptive and inferential statistics were conducted for two subgroups: 460 surveys assessing the healthy eating priority practice and 809 surveys assessing the physical activity priority practice.

All models included covariates for service size, state/territory, and service type. Logistic regression models were used to estimate whether implementation of priority practices varied over time (months 1–8). Ordinal logistic regression models were used to describe changes in barriers (i.e., median TDF domain scores) over time, with scores of 6 or higher indicating no barrier. Logistic regression was used to explore associations between barriers and implementation, adjusting for timepoints and covariates.

For the physical activity surveys, mixed models included a random effect for service ID to address repeated measures. To address sparse data among categories in the healthy eating practice surveys, we collapsed categories where appropriate. We then trialed using Bayesian ordinal regression to flexibly model these data.⁴³ Supplementary Data S3 contains further details on the statistical analysis.

Precision of estimates

This descriptive and exploratory study, with 809 survey responses, was able to estimate the proportion of services implementing the priority practices with a margin of error between 2.1% and 3.5% for proportions ranging from 0.1 to 0.5.

Results

Of the 1127 ECEC services randomly selected, eight services were permanently closed. Of the remaining 1119 eligible services, a total of 716 (64%) eligible services participated, with 93 (13%) completing two surveys, resulting in 809 surveys. Of the 716 services, 65% were from NSW, 13% from Victoria, 12% from Queensland, 5% from Western Australia, 2% from South Australia, and less than 2% from the Australian Capital Territory, Tasmania, and Northern Territory.

A total of 416 (58.1%) services, with 460 (56.9%) surveys, reported providing at least one meal and involvement in menu planning. There were 774 (95.7%) surveys completed by 683 (95.4%) services that answered items about educating and engaging families in their children’s physical activity. Table 1 provides the service characteristics and average TDF domain scores for the 716 participating services in relation to their implementation status.

Table 1.

Service Characteristics and Theoretical Domains Framework (TDF) Domain Scores for ECEC Services by Implementation of Priority Practices (N = 716)

Characteristic	Healthy eating practice (n = 416)		Physical activity practice (n = 683)		TotalservicesN = 716
Characteristic	Not implementingn = 329 (79.1%)^a	Implementingn = 87(20.9%)^a	Not implementingn = 207 (30.3%)^a	Implementingn = 476 (69.7%)^a	TotalservicesN = 716
Service size, median (interquartile range [IQR])
Children enrolled	85 (60, 116)	86 (64, 117)	78 (56, 113)	79 (52, 106)	79 (52, 109)
Educators	20 (13, 25)	20 (13, 25)	17 (11, 25)	15 (10, 23)	16 (10, 23)
Service type, n (%)
Long day care	318 (97%)	86 (99%)	196 (95%)	427 (90%)	655 (91%)
Preschool	11 (3.3%)	1 (1.1%)	11 (5.3%)	49 (10%)	61 (8.5%)
TDF domain scores (higher score represents that the domains are considered less of a barrier), median (IQR)
Knowledge	6.00 (6.00, 7.00)	7.00 (6.00, 7.00)	5.00 (3.00, 6.00)	6.00 (5.00, 6.00)	—
Missing	21	1	36	34
Skills	6.00 (6.00, 6.00)	6.00 (6.00, 6.50)	4.00 (3.00, 6.00)	6.00 (5.00, 6.00)	—
Missing	24	0	34	35
Beliefs about capabilities	6.00 (5.50, 6.00)	6.00 (6.00, 6.50)	4.00 (3.50, 5.50)	5.50 (4.50, 6.00)	—
Missing	29	2	33	35
Environmental context and resources	6.00 (6.00, 7.00)	6.00 (6.00, 7.00)	5.00 (4.00, 6.00)	6.00 (5.00, 6.00)	—
Missing	32	2	42	49
Social influences	6.00 (6.00, 6.50)	6.00 (6.00, 6.50)	6.00 (5.00, 6.00)	6.00 (6.00, 6.00)	—
Missing	33	4	38	45

Note: numbers may not add up to 100% due to missing data.

Median (IQR), n (%) within columns.

Services’ perceived implementation stage for meeting healthy menu standards was reported as precontemplation by 0.7%, contemplation by 4.1%, preparation by 11%, action by 33.1%, and maintenance by 51.1%. Services’ perceived implementation stage for the physical activity priority practice was reported as precontemplation by 23.7%, contemplation by 16.1%, preparation by 16.3%, action by 23.2%, and maintenance by 20.7%.

Implementation of Priority Practices, Over Time

Table 2 outlines the implementation over time, and this was not significant (months 1–8).

Table 2.

Implementation of Priority Practices Over Time (Logistic Regression)^a

	Healthy eating practice(N = 460)		Physical activity practice(N = 774)
	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p
Implementation over time
Month 1	Ref	—	Ref	—
Month 2	1.19 (0.48, 2.93)	0.7	1.20 (0.62, 2.34)	0.6
Month 3	0.80 (0.32, 2.05)	0.6	1.03 (0.54, 2.00)	>0.9
Month 4	0.79 (0.29, 2.17)	0.7	0.71 (0.37, 1.34)	0.3
Month 5	0.87 (0.33, 2.29)	0.8	0.95 (0.50, 1.81)	0.9
Month 6	0.94 (0.36, 2.47)	>0.9	1.11 (0.58, 2.14)	0.8
Month 7	1.23 (0.48, 3.16)	0.7	0.82 (0.44, 1.56)	0.6
Month 8	0.86 (0.34, 2.22)	0.8	0.91 (0.47, 1.75)	0.8

Adjusted for service size (n children), state/territory, and service type.

CI, confidence interval; OR, odds ratio; Cr, credible interval; pd, probability of direction.

Barriers to Implementing Priority Practices, Over Time

Table 3 displays the five barriers (i.e., TDF domain scores) to implementing the priority practices over time. In relation to meeting healthy menu standards for ECEC, there were changes over time in scores for barriers to meeting healthy menu standards for ECEC; however, there was no general trend in this relationship.

Table 3.

Barriers (i.e., Theoretical Domains Framework (TDF) Domain Scores^a) to the Implementation of Priority Practices Over Time

	Knowledge		Skills		Beliefs about capabilities		Environmental context and resources		Social influences
	Adjusted OR (95% Cr)	pd	Adjusted OR (95% Cr)	pd	Adjusted OR (95% Cr)	pd	Adjusted OR (95% Cr)	pd	Adjusted OR (95% Cr)	pd
Domain score over time, healthy menu planning standards (N = 460; Bayesian ordinal regression models)^b
Month 1	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Month 2	0.75 (0.56, 1.00)	0.97	1.66 (1.23, 2.23)	>0.99	0.86 (0.63, 1.18)	0.82	0.76 (0.56, 1.03)	0.96	0.70 (0.50, 0.97)	0.98
Month 3	0.70 (0.52, 0.93)	0.99	1.06 (0.79, 1.42)	0.64	1.02 (0.75, 1.41)	0.56	0.80 (0.59, 1.09)	0.91	0.61 (0.44, 0.85)	>.99
Month 4	0.78 (0.57, 1.06)	0.94	0.79 (0.58, 1.08)	0.93	0.99 (0.71, 1.38)	0.53	0.57 (0.41, 0.78)	>0.99	0.41 (0.28, 0.58)	>.99
Month 5	0.82 (0.61, 1.10)	0.9	1.23 (0.91, 1.65)	0.91	0.89 (0.65, 1.22)	0.77	0.83 (0.60, 1.13)	0.88	0.67 (0.48, 0.94)	0.99
Month 6	1.22 (0.90, 1.67)	0.9	1.31 (0.96, 1.80)	0.96	1.02 (0.73, 1.42)	0.54	1.18 (0.86, 1.64)	0.85	1.00 (0.71, 1.40)	0.51
Month 7	1.00 (0.74, 1.35)	0.51	1.70 (1.25, 2.33)	>.99	1.10 (0.79, 1.51)	0.71	0.71 (0.51, 0.99)	0.98	0.62 (0.43, 0.87)	>.99
Month 8	1.13 (0.84, 1.51)	0.79	1.65 (1.22, 2.22)	>.99	1.29 (0.94, 1.77)	0.94	0.78 (0.58, 1.07)	0.94	0.80 (0.57, 1.12)	0.9

	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p
Domain score over time, educating and engaging families in children’s physical activity (N = 774; ordinal regression models)^b
Month 1	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Month 2	0.77 (0.46, 1.30)	0.3	0.82 (0.45, 1.50)	0.5	0.91 (0.54, 1.53)	0.7	0.78 (0.45, 1.36)	0.4	0.55 (0.31, 0.99)	0.046
Month 3	1.13 (0.67, 1.91)	0.7	1.23 (0.71, 2.13)	0.4	1.47 (0.81, 2.65)	0.2	0.99 (0.54, 1.80)	>0.9	1.01 (0.55, 1.85)	>0.9
Month 4	0.93 (0.55, 1.58)	0.8	0.83 (0.50, 1.37)	0.5	0.77 (0.45, 1.31)	0.3	0.76 (0.43, 1.33)	0.3	0.77 (0.42, 1.43)	0.4
Month 5	1.04 (0.61, 1.75)	0.9	1.02 (0.59, 1.77)	>0.9	0.85 (0.51, 1.43)	0.5	0.76 (0.44, 1.32)	0.3	0.64 (0.36, 1.13)	0.12
Month 6	0.86 (0.50, 1.48)	0.6	1.04 (0.57, 1.90)	0.9	0.99 (0.56, 1.73)	>0.9	0.80 (0.46, 1.40)	0.4	0.74 (0.41, 1.33)	0.3
Month 7	0.79 (0.43, 1.45)	0.4	0.87 (0.50, 1.54)	0.6	0.91 (0.51, 1.61)	0.7	0.70 (0.41, 1.21)	0.2	0.53 (0.28, 1.00)	0.051
Month 8	0.92 (0.54, 1.57)	0.8	0.79 (0.42, 1.49)	0.5	0.84 (0.45, 1.56)	0.6	0.61 (0.32, 1.16)	0.13	0.74 (0.40, 1.35)	0.3

Higher domain score indicates that this is less of a barrier.

Adjusted for service size (n children), state/territory, and service type.

For Bayesian ordinal regression models: Bold values indicate high certainty of direction [probability of direction (pd) > 95%].

For ordinal regression models: Bold values indicate statistical significance (p < 0.05).

There was little significant change in any of the five barriers over time in relation to educating and engaging families in children’s physical activity, with no general pattern in the data.

Association Between Barriers and Implementation

Time was included in the models looking at the association between barriers and implementation of priority healthy eating and physical activity practices, but there was no change over time in the association in any model.

Implementation Status

Table 4 shows how barriers are associated with implementation status. As TDF domain scores increased (i.e., considered less of a barrier), the odds of implementing the priority practices significantly increased.

Table 4.

Associations Between Barriers (i.e., Theoretical Domains Framework (TDF) Domain Scores) and Implementation Status of Healthy Eating and Physical Activity Priority Practices, Over Each Time Point (Logistic Regression)^a

	Healthy eating practice (N = 460)		Physical activity practice (N = 774)
	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p
Knowledge	2.21 (1.51, 3.23)	<0.001	1.57 (1.40, 1.76)	<0.001
Skills	1.37 (1.07, 1.77)	0.014	1.61 (1.44, 1.80)	<0.001
Beliefs about capabilities	1.75 (1.25, 2.46)	0.001	1.61 (1.42, 1.83)	<0.001
Environmental context and resources	1.45 (1.01, 2.07)	0.044	1.57 (1.38, 1.80)	<0.001
Social influences	1.40 (0.98, 2.00)	0.067	1.45 (1.23, 1.71)	<0.001

Adjusted for service size (n children), state/territory, service type, and time (months 1–8).

Implementation stage

Table 5 shows that barriers to meeting healthy menu standards for ECEC varied according to services’ perceived implementation stage.

Table 5.

Association Between Implementation Stage and Barriers (i.e., Theoretical Domains Framework (TDF) Domain Scores)^a for Meeting Healthy Menu Standards (Logistic Regression)^b

	Knowledge		Skills		Beliefs about capabilities		Environmental context and resources		Social influences
	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p
Early stages (precontemplation, contemplation)	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Advanced stages (preparation, action, maintenance)	4.44 (1.66, 11.90)	0.003	2.27 (0.87, 5.88)	0.092	3.29 (1.28, 8.45)	0.014	4.96 (1.67, 14.7)	0.005	2.31 (0.93, 5.72)	0.071

Note: Results in bold text indicate 95% CI for the OR does not include 1.

Higher domain score indicates that this is less of a barrier.

All models adjusted for service size (n children), state/territory, service type, and time (months 1–8).

Table 6 shows a similar trend in the association between the implementation stage and barriers in relation to educating and engaging families in their children’s physical activity.

Table 6.

Association Between Implementation Stage and Barriers (i.e., Theoretical Domains Framework (TDF) Domain Scores)^a for Educating and Engaging Families in Children’s Physical Activity (Ordinal Regression)^b

	Knowledge		Skills		Beliefs about capabilities		Environmental context and resources		Social influences
	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p	Adjusted OR (95% CI)	p
Precontemplation	Ref	—	Ref	—	Ref	—	Ref	—	Ref	—
Contemplation	1.68 (1.08, 2.63)	0.022	1.78 (1.15, 2.75)	0.009	1.90 (1.22, 2.95)	0.005	1.95 (1.20, 3.15)	0.007	2.12 (1.24, 3.64)	0.007
Preparation	3.57 (2.24, 5.69)	<0.001	3.40 (2.12, 5.44)	<0.001	3.39 (2.14, 5.37)	<0.001	4.20 (2.63, 6.72)	<0.001	3.42 (2.03, 5.77)	<0.001
Action	5.76 (3.75, 8.87)	<0.001	8.26 (5.29, 12.9)	<0.001	5.27 (3.38, 8.21)	<0.001	6.97 (4.50, 10.8)	<0.001	3.93 (2.46, 6.28)	<0.001
Maintenance	14.2 (8.83, 22.7)	<0.001	14.7 (9.02, 23.9)	<0.001	11.1 (6.75, 18.3)	<0.001	13.3 (8.20, 21.5)	<0.001	9.49 (5.70, 15.8)	<0.001

Results in bold text indicate 95% CI for the OR does not include 1.

Higher domain score indicates that this is less of a barrier.

All models adjusted for service size (n children), state/territory, service type, and time (months 1–8).

Discussion

This study aimed to examine changes in the prevalence of, and barriers to, implementing healthy eating and physical activity priority practices in ECEC services over a period of 8 months. There was no statistically significant change over time in the implementation of these practices, nor, accordingly, any consistent trends in changes in barriers. This study found a clear trend with reduced barriers associated with increased implementation status and advanced implementation stages.

Although active implementation support can lead to improved implementation of such practices,¹⁸ the practices examined in this study were just some of many targets within state health promotion initiatives and may not have been the focus of current implementation efforts. Our previous research found significant changes in the implementation of such practices in a cohort of NSW ECECs over 7 years, suggesting that longer assessment may have yielded different findings.²⁶ Given the lack of changes in implementation, it is unsurprising that no clear trend emerged for changes to barriers over time. In the absence of a general trend, the few sporadic significant associations were treated with caution due to the sparse data.

This study found that as barriers in all five domains were reduced, the likelihood of services educating and engaging families in their children’s physical activity increased. Specifically, the median TDF domain scores were significantly lower for services not implementing the practice, suggesting they perceived more barriers than those that were implementing this practice. Similarly, for the healthy eating priority practice, all domains except social influences were significantly associated with implementation. These findings may indicate that addressing such barriers is particularly important when attempting to increase services’ implementation of evidence-based policies and practices.^16–18

The results suggest there could be potential ceiling effects with the TDF measure, which may have limited the ability to detect change over time. This is particularly true for the healthy eating priority practice with the median domain scores being relatively high, suggesting limited barriers, regardless of implementation status. Although validated,³⁹ the quantitative assessment provides limited insight into the specific barriers related to implementation. Future research should consider supplementing these measures with qualitative approaches.

This study overcomes the limitations of previous research which have typically been undertaken at a single time point often prior to implementation.¹⁹ Services reporting more advanced stages of implementing healthy menu standards, were less likely to report barriers related to knowledge, beliefs about capabilities, or environmental context and resources compared to services in the earlier stages, reinforcing the need to tailor implementation efforts to barriers. Training and provision of resources may be useful to target those barriers in the early stages, to assist in transitioning services to more advanced stages; however, different strategies may be needed in the later phases as different barriers start to emerge. For the physical activity practice, we found that across all TDF domains, higher domain scores (indicating less of a barrier) were observed moving from precontemplation into action stages. This consistency reinforces the importance of adequately understanding and targeting key barriers to implementation according to the implementation stage. Specifically, in the maintenance stage, the significantly higher domain scores (nine or more times higher than precontemplation phase) suggest that the examined constructs were no longer relevant barriers for ECEC services for educating and engaging parents in their children’s physical activity. Such findings support previous qualitative research suggesting that determinants of maintenance/sustainment may differ from those in the earlier implementation stages.^22,44 Future research exploring specific strategies associated with transitioning ECEC services across different implementation phases and including a wider range of barriers is needed to better support the design of a population-based program to improve nutrition and physical activity environments in ECEC services.

Limitations

The study did not track the same services over time, but repeated cross-sectional designs can provide aggregate estimates of population change and overcome limitations of cohort designs including attrition and participant burden.⁴⁵ Due to funding time frames, this study was conducted over a relatively short period potentially limiting the ability to detect change. As political and funding cycles can vary significantly in a short time,²⁴ and such changes can influence implementation, it was believed that population-level change was feasible in this timeframe. To reduce participant burden, the study focused on only two priority practices and the five most frequently reported barriers from previous research.^19,37,38 This approach might have missed barriers, particularly for services in the action and maintenance stages. Future studies that allow open-ended responses and assess a broader range of barriers could offer more detail.

Due to the number of statistical tests, the likelihood of spurious findings increased. The smaller sample size for the healthy eating practice resulted in sparse data, so more flexible Bayesian approaches were used. Caution was taken when interpreting the results by focusing on trends in the data and avoiding detailed discussion regarding differences between the two priority practices. However, the lower prevalence of services implementing the healthy eating practice may have also been a result of the more complex multicomponent criteria for the practice, as previous research also reported lower prevalence for multicomponent practices.¹⁴

Although the majority of Australian ECEC services are long day cares rather than preschools,⁴⁶ they were still overrepresented, so all models were adjusted for service type. We acknowledge the sampling bias from the overrepresentation of ECEC services in NSW and HNELHD. To mitigate the effects, we adjusted all models for state and territory. This sampling approach was intentionally designed to offer valuable feedback to our research partners—state and local early childhood health promotion teams—supporting their planning for future implementation efforts in ECEC services. The generalizability of the findings to other jurisdictions may be limited.

Conclusion

This study did not detect change over time in the prevalence of, or barriers to, implementation of priority healthy eating and physical activity practices by ECEC services. When assessing the association between barriers and implementation, we found that barriers appear to change over the implementation stages. These results highlight the need to continually monitor and address barriers to implementation throughout the implementation process, as they are likely to vary as services move through the different stages of implementation. Barriers assessments should be repeated to allow for continued adaptation of implementation support.

Impact Statement

To support ECEC services in delivering obesity prevention practices, we need an understanding of the dynamic nature of, and interaction between, implementation and barriers. This study is one of the first to find that barriers vary across stages of implementation, indicating a need for continual monitoring and adaptation of support.

Footnotes

Acknowledgments

The authors would like to thank the nominated supervisors of the ECEC services that participated in this research. They would also like to thank the Centre for Population Health NSW for their input on the advisory group and for providing contextual information in relation to the NSW ECEC environment. The authors also acknowledge the casual telephone interviewers who contributed to data collection, including Hanh My Thi Nguyen and Michelle Lim.

Authors’ Contributions

A.A.: Article conceptualization, acquisition of data, data interpretation, drafting article, and editing. M.H.: Data analysis, data interpretation, drafting article, reviewing, and editing. L.W., A.G., C.O., M.L., H.C., G.S., J.W., and R.H.: Study conceptualization, data interpretation, reviewing, and editing. M.R. and R.L.: Acquisition of data, data interpretation, reviewing, and editing. K.G.: Provided infrastructure for acquisition of data and reviewing. S.L.Y.: Funding acquisition, study and article conceptualization, acquisition of data, data interpretation, drafting article, reviewing, and editing. All authors read and approved the final article.

Author Disclosure Statement

The authors declare that they have no competing interests.

Funding Information

This study was funded by The Australian Prevention Partnership Centre, administered by the Sax Institute, as part of the Rapid Response Research Grant Program, an NHMRC Center for Research Excellence grant (CIA: Wolfenden, APP1153479), and project funds awarded to S.L.Y. as part of Heart Foundation Future Leader Fellowship (106654). Funders had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the article.

Supplementary Material

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