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Few Australian childcare centers provide foods consistent with sector dietary guidelines. Digital health technologies are a promising medium to improve the implementation of evidence-based guidelines in the setting. Despite being widely accessible, the population-level impact of such technologies has been limited due to the lack of adoption by end users.
This study aimed to assess in a national sample of Australian childcare centers (1) intentions to adopt digital health interventions to support the implementation of dietary guidelines, (2) reported barriers and enablers to the adoption of digital health interventions in the setting, and (3) barriers and enablers associated with high intentions to adopt digital health interventions.
A cross-sectional telephone or online survey was undertaken with 407 childcare centers randomly sampled from a publicly available national register in 2018. Center intentions to adopt new digital health interventions to support dietary guideline implementation in the sector were assessed, in addition to perceived individual, organizational, and contextual factors that may influence adoption based on seven subdomains within the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health and care technologies framework. A multiple-variable linear model was used to identify factors associated with high intentions to adopt digital health interventions.
Findings indicate that 58.9% (229/389) of childcare centers have high intentions to adopt a digital health intervention to support guideline implementation. The
A substantial proportion of Australian childcare centers have high intentions to adopt new digital health interventions to support dietary guideline implementation. Given evidence of the effectiveness of digital health interventions, these findings suggest that such an intervention may make an important contribution to improving public health nutrition in early childhood.
Poor diet is a modifiable risk factor and leading cause of burden of disease globally [
Digital health interventions (eg, web-based programs, apps, etc) are advocated by the World Health Organization [
Broadly, systematic reviews, guidelines, and previous literature suggest that factors across a number of levels are important for the adoption and implementation of digital health interventions. These include factors related to the individual user (eg, knowledge, skills, beliefs, and attitudes) [
Within ECEC settings, a 2015 systematic review examining the barriers to integration of information technology more broadly, including computers, tablets, and touchscreen whiteboards, identified a scarcity of empirical studies examining barriers and enablers within the setting, none of which focused on improving guideline implementation or child health outcomes [
As such, by employing the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health and care technologies framework [
This study employed a cross-sectional design. Ethical approval was obtained by the Human Research Ethics Committees of Hunter New England (16/02/17/4.05) and the University of Newcastle (H-2016-0111). All subjects in this research study provided consent to participate.
The Australian Children’s Education and Care Quality Authority’s (ACECQA) national register [
Childcare center eligibility was assessed via online or telephone survey items. Centers were deemed ineligible if they did not provide meals to children or make menu planning decisions onsite, as this survey was assessing technology to support nutrition guideline implementation on menus; had staff with insufficient English to complete the survey; were a Department of Education and Communities center, as ethical approval was not obtained from the relevant government department; were located in the Hunter New England region of NSW or were select centers across NSW, due to concurrent nutrition and physical activity research trials being undertaken by the research team; were identified as out-of-school hours, vacation care, or family day care; or catered solely to children with special needs.
An email with an information statement and link to an online survey was sent to the nominated supervisor (ie, the center manager) of all sampled childcare centers (N=1500) inviting them to assess eligibility and participate in the study. Nominated supervisors were able to select an alternate staff member (eg, center director) to complete the survey on their behalf. Centers that did not complete the survey within 4 weeks were sent a reminder email to participate (1466/1500, 97.73%), followed by a phone call from a member of the research team (1455/1500, 97.00%) to assess eligibility and gain verbal consent to complete the telephone version of the survey. A final reminder email was sent to centers that indicated a preference to complete the online version of the survey (846/1500, 56.40%) and those who were noncontactable via phone. Centers that were yet to complete the survey following the final reminder email received a final telephone call to gain consent and complete a telephone version of the survey (744/1500, 49.60%). Centers were not offered any incentives to complete the survey. Data to assess study outcomes were collected between January and August 2018.
Childcare centers were asked to report on the type of center (ie, preschool or long day care), number of full-time equivalent staff members, center opening and closing hours, number of children enrolled, and the number of children enrolled identifying as of Aboriginal and/or Torres Strait Islander background. Childcare center staff completing the survey were asked to report their main role at the center and the total number of years working in the childcare setting. Survey items assessing center characteristics were sourced from previous Australian childcare center surveys conducted by the research team [
Center geographical information, including state and postcode, were obtained via the ACECQA national register to determine location and the center area socioeconomic classification.
To aid comprehension and standardization of digital health interventions and their capabilities, participants were first given a brief example of the potential modality (eg, web-based or online) and key features (eg, feedback and tips) that could be provided within a digital health intervention to support guideline implementation in the setting. Three survey items derived from the Technology Acceptance Model [
A purpose-built measure based on the NASSS framework by Greenhalgh [
An expert advisory group, including health promotion practitioners, implementation scientists, and dietitians, was involved in the development of the measure. Based on expert advisory group consensus, only three of the seven NASSS domains were deemed relevant to the end users for the scale of dissemination of digital health interventions under examination and were, therefore, assessed. At the time of survey development, no validated measure for the NASSS framework existed. As such, a search was conducted for validated measures that had corresponding domains to the NASSS framework. Where possible, such validated measures were employed and adapted to fit the ECEC context, including the e-Health Readiness Measure [
Nonadoption, abandonment, scale-up, spread, and sustainability framework application to the early childhood education and care setting.
Domain and subdomain (No. of items) | Example survey item | |
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Changes in staff roles, practices, and identities (3 items) | Using an online program is consistent with the usual practices of my cook and menu planner. |
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Organization’s capacity to innovate (6 items) | Overall, I think our service has a champion or leader for using new technology. |
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Readiness of the organization for technology-supported change (4 items) | Overall, I think our service has access to experts in use of new technology. |
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Ease of the adoption and funding decision (1 item) | It would be easy to adopt new technology to support menu planning in my service. |
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Changes needed in team interactions and routines (2 items) | My service would need to change the way it currently plans menus if we decided to adopt new technology. |
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Identifying work and individuals involved in implementation (2 items) | We already have the existing personnel available to support the adoption of new technology. |
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Political, economic, regulatory, professional (eg, medicolegal), and sociocultural context for program rollout (6 items) | I would be more likely to adopt new technology in my service if it was promoted by relevant government agencies (ie, Department of Education or Department of Health). |
All analyses were performed in SAS, version 9.3 (SAS Institute) [
An intention-to-adopt score for each responder was calculated by averaging scores for the three intention items. This score was also used to dichotomize responders into having low intentions to adopt (score <6) or high intentions to adopt (score ≥6). This cut point corresponds to those who agree or strongly agree with each item. Such an approach has been used previously within ECEC centers [
Similar to previous studies assessing barriers and enablers using theoretical frameworks [
All seven NASSS subdomains were entered as independent variables into a multiple-variable logistic regression model, to assess which NASSS constructs were significantly associated with high intentions to adopt digital health interventions (ie, dependent variable) after adjusting for each other. The significance value was set at .05.
Of the 1500 centers invited to participate in the study, 72 (4.80%) were noncontactable, 53 (3.53%) were contacted but did not respond, and 378 (25.20%) declined to participate prior to eligibility being assessed. A total of 997 out of 1500 (66.47%) centers consented to the study and were assessed for eligibility, with 590 of these 997 (59.2%) centers deemed ineligible, most commonly due to the center not providing meals and/or snacks to children and being a Department of Education and Community center. This resulted in a total of 407 centers taking part in the survey. There were no statistically significant differences in center socioeconomic area between consenters and nonconsenters.
The large majority of participating centers were long day care centers (391/407, 96.1%) (see
Childcare center and responder characteristics.
Characteristics | Value, n (%) or mean (SD) | ||
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Preschool | 16 (3.9) |
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Long day care center | 391 (96.1) |
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Number of children enrolled (n=406), mean (SD) | 96.33 (56.79) | |
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Number of full-time equivalent primary contact teaching staff (n=404), mean (SD) | 12.78 (7.93) | |
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Number of children of Aboriginal and/or Torres Strait Islander background enrolled at center (n=406), n (%) | 214 (52.7) | |
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High | 231 (56.8) |
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Low | 176 (43.2) |
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Urban (major cities) | 307 (75.4) |
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Rural (inner regional, outer regional, or remote) | 100 (24.6) |
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New South Wales | 165 (40.5) |
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Victoria | 94 (23.1) |
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Queensland | 62 (15.2) |
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Australian Capital Territory | 7 (1.7) |
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Tasmania | 7 (1.7) |
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Western Australia | 40 (9.8) |
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South Australia | 25 (6.1) |
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Northern Territory | 7 (1.7) |
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Nominated supervisor | 183 (45.9) |
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Director | 179 (44.9) |
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Cook | 12 (3.0) |
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Other | 28 (7.0) |
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≤5 | 36 (9.1) |
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6-10 | 83 (20.9) |
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>10 | 278 (70.0) |
The mean intention score was 5.52 (SD 1.07), with a median of 6.00 (IQR 5.00-6.00). Of 389 responders, 229 (58.9%) centers had high intentions to adopt digital health interventions to support the implementation of dietary guidelines.
A mean score of 4 or lower (ie, barriers) was found for four of the seven NASSS domains (see
Mean and median scores for the nonadoption, abandonment, scale-up, spread, and sustainability subdomain barriers and enablers, as reported by responders.
Barrier or enabler | Scorea | ||
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Mean (SD)b | Median (IQR) | |
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Changes in staff roles, practices, and identities | 4.32 (1.25) | 4.33 (3.33-5.00) |
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Organization’s capacity to innovate (n=382) | 5.25 (1.00) | 5.50 (4.67-6.00) |
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Readiness of the organization for technology-supported change (n=386) | 4.88 (1.03) | 5.00 (4.25-5.75) |
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Ease of the adoption and funding decision (n=387) | 5.22 (1.31) | 6.00 (4.00-6.00) |
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Changes needed in team interactions and routines (n=389) | 3.52 (1.30) | 3.50 (2.50-4.00) |
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Identifying work and individuals involved in implementation (n=389) | 4.35 (1.19) | 4.00 (4.00-5.00) |
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Political, economic, regulatory, professional (eg, medicolegal), and sociocultural context for program rollout | 5.07 (1.08) | 5.33 (4.50-6.00) |
aConstructs are reported on a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree).
bA mean of ≤4 suggests that the particular domain may be a barrier; a mean of >4 suggests the domain may be an enabler.
Multiple-variable logistic regression analyses revealed a significant association between two of the NASSS subdomains and high intentions to adopt digital health interventions (see
Nonadoption, abandonment, scale-up, spread, and sustainability subdomains associated with high intentions to adopt digital health interventions in early childhood education and care centers.
Barrier or enabler | Odds ratio | 95% CI | ||
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Changes in staff roles, practices, and identities | 0.88 | 0.71-1.10 | .27 |
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Organization’s capacity to innovate | 1.26 | 0.91-1.75 | .17 |
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Readiness of the organization for technology-supported change | 1.15 | 0.83-1.59 | .41 |
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Ease of the adoption and funding decision | 1.75 | 1.40-2.18 | <.001 |
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Changes needed in team interactions and routines | 0.92 | 0.75-1.13 | .42 |
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Identifying work and individuals involved in implementation | 1.46 | 1.16-1.84 | .001 |
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Political, economic, regulatory, professional (eg, medicolegal), and sociocultural context for program rollout | 1.03 | 0.82-1.29 | .81 |
This novel study applied a technology-specific framework to conduct a theoretical assessment of childcare center barriers and enablers to the adoption of digital health interventions to improve dietary guideline implementation, nationally. Application of the NASSS framework resulted in the identification of a number of reported barriers and enablers. The main barrier identified was
The study found that over half (229/389, 58.9%) of responders had high intentions to adopt digital health interventions in the setting. Few studies of technology-based health interventions within the ECEC setting report adoption rates, with variable findings. A 2015 cross-sectional study assessing intentions to adopt a web-based program to support healthy eating and physical activity policies and practices in the ECEC setting reported that 72% of respondents had high intentions to adopt such a program [
When examining the potential barriers and enablers to adoption of digital health interventions, scores of 4 or higher were found for only three of the subdomains assessed (ie, enablers), two of which fall within the organizational construct of the NASSS framework. The highest levels of agreement were found for the
Study results revealed a discrepancy in the reported barriers and enablers to adoption of digital health interventions and the factors associated with adoption. Multiple-variable logistic regression analyses determined that the
While recent studies have employed the NASSS framework retrospectively to categorize various constructs [
The intention to adopt digital health interventions, rather than actual adoption, was assessed. While there is evidence of a relationship between intentions and actual adoption [
This study provides novel insights into the perceived and actual factors that may facilitate or impede the adoption of digital health interventions at scale from the perspective of end users. A substantial proportion of Australian childcare centers reported high intentions to adopt digital health interventions. Given evidence of the effectiveness of such technologies, these interventions have the potential to make an important contribution to improving public health nutrition in early childhood. Nonetheless, future efforts to disseminate digital health prevention programs at scale should consider targeting factors within the
Australian Children’s Education and Care Quality Authority
Cancer Council NSW
early childhood education and care
Hunter New England Population Health
nonadoption, abandonment, scale-up, spread, and sustainability
National Health and Medical Research Council
New South Wales
The Australian Prevention Partnership Centre
The authors wish to thank participating childcare centers, data collection staff, and the expert advisory group. This project is funded in part by The Australian Prevention Partnership Centre (TAPPC), the National Health and Medical Research Council (NHMRC) (APP1102943), the Cancer Council NSW (CCNSW) (PG16-05), and the Priority Research Centre and School of Medicine and Public Health, University of Newcastle. TAPPC, NHMRC, and CCNSW played no role in the conduct of the research. The content of this publication is the responsibility of the authors and does not reflect the views of TAPPC, NHMRC, or CCNSW. Hunter New England Population Health (HNEPH) and the University of Newcastle provided infrastructure funding. LW is a Hunter New England Clinical Research Fellow and is supported by a Heart Foundation Future Leader Fellowship (award No. 101175) and an NHMRC Career Development Fellowship (APP1128348). SY is a postdoctoral research fellow funded by the National Heart Foundation (award No. 100547) and the Australian Research Council (DE170100382). CB is supported by a cofunded industry scholarship between HNEPH and the University of Newcastle.
All authors contributed to conception or design of the work, data acquisition, and analysis or interpretation of data, and took part in revising the manuscript. All authors give their final approval of this version to be published and agree to be accountable for all aspects of the work. AG, SY, and LW conceived the study and secured funding. AG and SY designed the evaluation procedures. AG and CB lead the acquisition of data. CL conducted the data analysis. AG led the drafting of the manuscript.
None declared.