This paper is in the following e-collection/theme issue:

Web-based and Mobile Health Interventions (3297)

Published on in Vol 17, No 5 (2015): May

Childcare Service Centers’ Preferences and Intentions to Use a Web-Based Program to Implement Healthy Eating and Physical Activity Policies and Practices: A Cross-Sectional Study

Childcare Service Centers’ Preferences and Intentions to Use a Web-Based Program to Implement Healthy Eating and Physical Activity Policies and Practices: A Cross-Sectional Study

Childcare Service Centers’ Preferences and Intentions to Use a Web-Based Program to Implement Healthy Eating and Physical Activity Policies and Practices: A Cross-Sectional Study

Authors of this article:

Sze Lin Yoong1, 2 Author Orcid Image ;   Christopher Michael Williams2, 3 Author Orcid Image ;   Meghan Finch1 Author Orcid Image ;   Rebecca Wyse1, 2 Author Orcid Image ;   Jannah Jones1, 2 Author Orcid Image ;   Megan Freund1 Author Orcid Image ;   John Henry Wiggers1, 2 Author Orcid Image ;   Nicole Nathan1, 2 Author Orcid Image ;   Pennie Dodds2 Author Orcid Image ;   Luke Wolfenden1, 2 Author Orcid Image
Article Authors Cited by (23) Tweetations (2) Metrics

    Original Paper

    1Hunter New England Local Health District, Hunter New England Population Health, Wallsend, Australia

    2The University of Newcastle, School of Medicine and Public Health, Callaghan, Australia

    3The George Institute for Global Health, Sydney, Australia

    Corresponding Author:

    Sze Lin Yoong, PhD

    Hunter New England Local Health District

    Hunter New England Population Health

    Longworth Avenue

    Wallsend, 2287

    Australia

    Phone: 61 249296413

    Fax:61 249246490

    Email: serene.yoong@hnehealth.nsw.gov.au


    Background: Overweight and obesity is a significant public health problem that impacts a large number of children globally. Supporting childcare centers to deliver healthy eating and physical activity-promoting policies and practices is a recommended strategy for obesity prevention, given that such services provide access to a substantial proportion of children during a key developmental period. Electronic Web-based interventions represent a novel way to support childcare service providers to implement such policies and practices.

    Objective: This study aimed to assess: (1) childcare centers’ current use of technology, (2) factors associated with intention to use electronic Web-based interventions, and (3) Web-based features that managers rated as useful to support staff with implementing healthy eating and physical activity-promoting policies and practices.

    Methods: A computer-assisted telephone interview (CATI) was conducted with service managers from long day care centers and preschools. The CATI assessed the following: (1) childcare center characteristics, (2) childcare centers’ use of electronic devices, (3) intention to use a hypothetical electronic Web-based program—assessed using the Technology Acceptance Model (TAM) with ratings between 1 (strongly disagree) and 7 (strongly agree), and (4) features rated as useful to include in a Web-based program.

    Results: Overall, 214 service centers out of 277 (77.3%) consented to participate. All service centers except 2 reported using computers (212/214, 99.1%), whereas 40.2% (86/214) used portable tablets. A total of 71.9% (151/210) of childcare service managers reported a score of 6 or more for intention to use a hypothetical electronic Web-based program. In a multivariable logistic regression analysis, intention to use the program was significantly associated with perceived ease of use (P=.002, odds ratio [OR] 3.9, 95% CI 1.6-9.2) and perceived usefulness (P<.001, OR 28,95% CI 8.0-95.2). Features reported by service managers as useful or very useful for a Web-based program included decision-support tools to support staff with menu planning (117/129, 90.7%), links to relevant resources (212/212, 100%), updated information on guidelines (208/212, 98.1%), and feedback regarding childcare center performance in relation to other childcare centers (212/212, 100%).

    Conclusions: Childcare service managers reported high intention to use a Web-based program and identified several useful features to support staff to implement healthy eating and physical activity policies and practices. Further descriptive and intervention research examining the development and use of such a program to support childcare centers with the implementation of healthy eating and physical activity-promoting policies and practices is warranted.

    J Med Internet Res 2015;17(5):e108

    doi:10.2196/jmir.3639

    Keywords

    obesity; long day care centers; childcare centers; guideline adherence 


    Overweight and obesity adversely impacts a large proportion of the population globally, accounting for at least 2.8 million deaths annually and 35.8 million disability-adjusted life years [1]. It is estimated that approximately 60% of adults in developing countries are overweight or obese [2]. Globally, approximately 43 million preschool-aged children were overweight or obese in 2010, with this figure projected to reach 60 million by 2020 [3]. Children who are overweight or obese are up to ten times more likely to develop non-insulin-dependent diabetes and eight times more likely to develop cardiovascular disease during childhood [4]. Overweight or obese children also have a significantly increased risk of adult morbidity and up to three times increased risk of adult mortality as compared to children within the healthy weight range [4]. As a consequence, interventions to reduce childhood overweight and obesity are recommended to reduce the risk of chronic disease in both childhood and adulthood [5,6].

    Center-based childcare services represent a promising setting for obesity prevention interventions targeting young children, with between 60% and 80% of young children in countries including Australia and the United States attending these centers [7,8]. Recognizing this opportunity, guidelines for childcare centers recommend the implementation of healthy eating and physical activity-promoting policies and practices [9,10]. Findings from reviews of randomized and quasi-experimental trials suggest that the implementation of a number of policies and practices in childcare, including providing programmed time for physical activity and improving nutritional quality of food provided, are effective in improving child diet and physical activity levels, and preventing unhealthy weight gain [5,11,12].

    Although research supports the implementation of healthy eating and physical activity policies and practices in childcare service settings [9,10], their adoption by childcare centers is suboptimal. A study conducted in 20 childcare centers in the United States found that approximately 30% of childcare centers met guideline recommendations for the provision of fruit and vegetables [13]. Further, in a sample of 96 childcare centers in the United States, only 14% of childcare centers provided 120 minutes of active play per day and 40% provided two or more occasions of teacher-led physical activity [14]. Similarly, a study of 261 Australian childcare centers reported that only 41% of preschools and 48% of long day care centers had a written physical activity policy, and between 46% and 60% undertook daily, programmed, fundamental movement skills programs for children aged 2 to 3 years and 3 to 5 years [15].

    A small number of trials have been conducted to improve the implementation of healthy eating and physical activity-promoting practices in the childcare setting [16,17]. Interventions which included multiple organizational change strategies, such as the provision of regular face-to-face or telephone support by qualified health staff and the provision of feedback and resources, have been shown to be effective [17-19]. However, such interventions are often resource intensive, and expensive to deliver to large numbers of centers. Most previous trials have also been conducted on a small number of childcare centers (ie, less than 30) [16,20,21], providing limited information regarding the effectiveness of such interventions when scaled up and delivered to all eligible centers [22].

    Web-based interventions, including the provision of online training and resources, and interactive tools, represent a promising way of providing population-wide support to childcare centers. Such interventions enable the provision of support to large numbers of childcare centers at a fraction of the cost of other modalities. Further, childcare centers report having existing computing infrastructure and are familiar with the use of Web-based technology, thus increasing the likelihood of engaging in such interventions [8,23,24]. Research examining the effectiveness of such electronic interventions in facilitating the implementation of health-promoting policies and practices in community-based settings is scarce, with an updated Agency for Healthcare and Research Quality review failing to identify such interventions in the childcare setting [25]. Research from primary care and hospital settings, however, demonstrate that electronic interventions can be used to improve clinicians’ practices through decision-support tools, performance monitoring and feedback, information communication and prompts, and reminder functions [26,27].

    Despite the promise of using electronic modalities, the public health impacts of such interventions are often impeded by low uptake and dropout or attrition in use [28,29]. Frequently reported barriers to uptake include the lack of access to appropriate infrastructure, setup costs, limited integration with existing operating electronic systems, lack of considered implementation, and failure of interventions to meet the immediate needs of users (eg, program being too complex and including features not acceptable or relevant to end users [13,30]) [29,31]. Recognizing such challenges, theories such as the Technology Acceptance Model (TAM) recommend that formative examination of factors associated with end-user intention to use a new electronic system be conducted to maximize the likelihood of end-user adoption [32,33]. Further, assessment of users’ preferences regarding the content and type of features they would like available in a Web-based intervention is likely to facilitate uptake and ongoing user engagement [13,34].

    To provide relevant information to guide the design and implementation of a Web-based intervention for childcare centers, a survey was conducted with childcare center managers to (1) identify centers’ access to the Web and Web-access devices, and (2) identify factors associated with managers’ intention to use a Web-based program designed to support implementation of healthy eating and physical activity-promoting policies and practices. Further, the study examined managers’ preferences for features to include in a Web-based program to support the implementation of such policies and practices in childcare centers.


    Ethics Approval

    Ethics approval was obtained from Hunter New England Local Health District (HNELHD) Human Research Ethics Committee (06/07/26/4.04) and the University of Newcastle (H-2008-0341).

    Design and Setting

    A cross-sectional study was conducted in the state of New South Wales (NSW), Australia. There were approximately 566,862 children aged between 0 and 5 years [35], and 2587 childcare centers—preschools and long day care centers—in the state [36].

    Sample

    Childcare centers, including preschools and long day care centers, in NSW provide education and center-based care for children aged 0 to 5 years. Childcare centers in NSW were identified from a list of all licensed centers supplied by the regulator—The State Office of Childcare. Using the RAND function in Microsoft Excel, a random sample of 277 out of 2587 (10.71%) eligible childcare centers within NSW were selected to participate in the study. Childcare centers that catered exclusively to children with special needs or that operated within a primary school were ineligible for participation. Childcare centers located within a particular region of the state were also excluded due to their participation in a separate implementation trial [37]. Licensing and accreditation requirements regarding healthy eating and physical activity are identical for both preschools and long day care centers [38]. As such, preferences for Web-based features to support implementation of obesity prevention policies and practices were not examined separately for the two types of childcare centers. In Australia, all childcare centers that provide government-subsidized childcare benefits are required by federal legislation to use a government-mandated, Web-based Child Care Management System (CCMS) to log enrolments, store essential service information, and enable calculation of childcare benefits [24]. All centers, regardless of whether they had access to CCMS software, were eligible to participate in this study.

    Recruitment and Data Collection

    Service managers of all selected childcare centers were sent an information letter inviting them to participate in the study survey, which was conducted from October to December 2013. Up to 2 weeks following the mailing of the invitations, research assistants telephoned the service managers to confirm eligibility and to gain consent. If a service manager was unavailable to complete the survey, an alternative staff member was nominated to participate in the survey. Data was collected via a 25-minute computer-assisted telephone interview (CATI).

    Measures

    Service Characteristics

    Service managers were asked to report the following: the center operating times, number of children enrolled, number of educators (ie, carers with primary contact role), and whether they were a childcare benefit-approved center using CCMS to report child enrolment details.

    Access and Use of Internet and Associated Equipment

    Service managers were asked to report whether they had access to the Internet, and whether they used computers and tablets in their center. If such infrastructure existed, service managers were asked to identify the purposes for using such infrastructure including the following: administrative tasks, completion of reporting requirements, staff education, child education, searching for information, accessing emails, recording and planning of daily program and activities, reporting progress to parents, taking photographs of children’s activities, and other purposes.

    Factors Associated With Intention to Use a Web-Based Program to Implement Policies and Practices

    Items from the Technology Acceptance Model were used to assess intention to use the hypothetical electronic Web-based program. TAM is one of the most parsimonious models assessing end-user intentions to adopt a new information technology system [39]. This model posits that attitudinal characteristics of end users, including perceived usefulness and perceived ease of use, are predictors of intentions to adopt new information technology systems. Reviews of empirical studies report that TAM accounts for between 40% and 70% of variance in explaining intention to use new electronic systems in health care, university, and commercial work settings [33,39,40]. Such studies report high internal consistency for each item (ie, Cronbach alpha >.8 for each item) [39]. This model has also shown high validity when used in countries external to Northern America, where originally developed [41], including China [42] and Switzerland [43]. Further, systematic reviews also report positive associations between behavioral intention to use and TAM constructs, with actual use of program [44,45]. Specifically, systematic reviews examining the relationship between behavioral intentions—as measured by TAM—and actual use of technology found a positive association for approximately 90% of included studies [44,45].

    Similar to other studies utilizing TAM [46], service managers were asked to rate on a 7-point scale—1 (strongly disagree) to 7 (strongly agree)—the perceived usefulness, perceived ease of use, and intention to use an electronic online system to support implementation of healthy eating and physical activity policies and practices. The questionnaire was pilot-tested with 6 childcare center managers and, as a result, some minor modifications to the wording of the questions were made to increase relevance to the setting. Perceived usefulness (ie, the perception that using this system will help users achieve gains in job performance) [41] was assessed by the following statements: It would be useful to assist staff, improve staff performance, increase staff productivity, and enhance effectiveness of staff delivery of healthy eating and physical activity-promoting policies and practices. Perceived ease of use (ie, the degree of ease associated with using a system) [41] examined the following: ease of interaction with the program, mental effort required, ease of use of program, and ease to get the program to do what they wanted it to do. Intention to use were assessed by asking managers whether they: intended to use the system, predicted they would use the system, and planned to use such a system if it were made available to them (see Multimedia Appendix 1 for questionnaire).

    Features to Support Healthy Eating and Physical Activity Policy and Practice Implementation

    Center managers were asked to rate on a 4-point scale—1 (very unhelpful) to 4 (very helpful)—whether they perceived the following features as helpful to support staff implement healthy eating and physical activity-promoting policies and practices: interaction and communication tools, including (1) chat rooms, (2) discussion boards, and (3) email feedback or phone support from health care service staff, provision of educational materials (ie, lunchbox or menu-planning ideas, physical activity ideas, links to other helpful websites), decision-support systems (ie, menu-planning tool), performance feedback and monitoring tools (ie, features to monitor progress over time and in relation to other services, and tools to help staff with prioritizing tasks), and prompts and reminders [18]. Such questions were based on consultations with childcare center managers, health promotion practitioners who support services to implement such practices, and a review of the literature examining Web-based applications used to support practice change in other settings [47,48].

    Statistical Analysis

    Descriptive statistics were generated for service characteristics, access, and use of electronic devices. Childcare centers with postcodes ranked in the top 50% of NSW postcodes based on their socioeconomic status (SES) were grouped as being located in higher socioeconomic areas, while those in the lower 50% were categorized as being located in lower socioeconomic areas using the 2009 Socio-Economic Indexes For Areas (SEIFA), Australia. Childcare centers were categorized as either rural (ie, those located in outer regional, remote, and very remote areas) or urban (ie, those in regional cities and inner regional areas) based on their postcode using the Accessibility/Remoteness Index of Australia (ARIA).

    Similar to that previously used in other studies, TAM subscale scores—perceived usefulness, perceived ease of use, and intention to use—were derived by summing responses—1 to 7—to all items in the subscale and dividing by the number of items within the scale [46]. Descriptive statistics, including mean and standard deviation, and median and interquartile range (IQR), were reported. TAM subscales were dichotomized into a score of 1 (strongly disagree) to 5.9 (slightly agree) or more than or equal to 6 (strongly agree or agree). This cut-point was chosen based on the median score of the subscales and corresponds to those who agree or strongly agree with the items examined within the subscales, providing a clinically meaningful way of interpreting the results.

    To examine factors associated with intention to use, a multivariable logistic regression was conducted using the backward stepwise method to exclude variables where P>.1. The dependent variable was intention to use, and factors examined were the following: perceived ease of use, perceived usefulness, locality (rural/urban), socioeconomic status (high/low), service type (preschool/nonpreschool), number of children enrolled, number of primary contact staff, number of computers, and whether the center used a childcare management enrolment system software. All significance tests were two-tailed, with an alpha of .05. The proportion and 95% confidence intervals of center managers reporting a system feature as useful/very useful in assisting with the implementation of healthy eating and physical activity-promoting policies and practices were calculated.


    Service Characteristics

    Overall, 277 centers were approached, and 214 (77.3%) consented to participate in the telephone survey. Of the participating centers, 36.9% (79/214) were preschools and 70.6% (151) were long day care centers (see Table 1). Of the centers, 7.5% (16/214) were both preschools and long day care centers. Almost all centers were open for 5 days per week and 77.6% (166/214) were open 8 or more hours per day. Of the centers, 81.8% (175/214) reported being approved under the childcare benefit scheme, and of those, 159/175 (90.9%) reported using a CCMS-approved software program to manage enrolments.

    Table 1. Descriptive characteristics of participating center-based childcare services (n=214).
    Service characteristics n (%) or mean (SD)
    Preschools, n (%)79 (36.9)
    Long day care centers, n (%)151 (70.6)
    Number of children enrolled, mean (SD)77 (34)
    Usual number of primary contact educators, mean (SD)8.9 (5.2)
    Average daily opening hours, mean (SD)9.6 (1.9)
    Open 5 days per week, n (%)207 (96.7)
    Open 8 hours or more per day, n (%)166 (77.6)
    Used CCMSasoftware, n (%)159 (74.3)

    aChild Care Management System (CCMS).

    Access and Use of Internet and Associated Equipment

    All but two services reported using computers at their center (212/214, 99.1%), with 58.9% (126/214) of all services reporting having access to three or more computers in their center. A total of 40.2% (86/214) of service managers reported using portable tablets in their center. Almost all centers (205/214, 95.8%) had Internet access for at least 1 year and the majority (160/214, 74.8%) reported having Internet access for 5 or more years. More than 90% of service managers reported using computers for administrative and reporting tasks (210/212, 99.1%), to search for information (204/212, 96.2%), and to access emails (206/212, 97.2%), whereas portable tablets were used most frequently to assist with child education (76/86, 88%) (see Table 2).

    Table 2. Use of computers and portable tablets by childcare center staff.
    TasksComputer (n=212),
    n (%, 95% CI)    		Portable tablet (n=86),
    n (%, 95% CI)
    Administrative tasks210 (99.1, 97.9-100)9 (10, 4-17)
    To complete reporting requirements210 (99.1, 97.8-100)11 (13, 6-20)
    For staff education193 (91.0, 87.2-94.9)22 (26, 16-35)
    To assist with child education in the classroom174 (82.1, 76.9-87.2)76 (88, 82-95)
    To search for information204 (96.2, 93.6-98.8)42 (49, 38-60)
    To access emails206 (97.2, 94.9-9.49)12 (14, 7-21)
    For planning and recording daily programming of service and children\'s activities180 (84.9, 80.1-89.7)34 (40, 29-50)
    To report progress and provide information to parents or staff187 (88.2, 83.9-92.5)19 (22, 13-31)
    Othera2 (0.9, 0-2.2)8 (9, 3-16)

    aIncludes use for networking or linking to other services, to play music, as a communication aid for special-needs children, and to display graphs.

    Factors Associated With Intention to Use a Web-Based Program to Implement Policies and Practices

    The mean score for intention to use a Web-based program (n=210) was 5.9 (SD 1.5), for perceived usefulness was 5.3 (SD 1.6), and for perceived ease of use was 5.5 (SD 1.1). The median score for intention to use was 6.0 (IQR 5.3-7.0), for perceived usefulness was 5.7 (IQR 4.8-6.8), and for perceived ease of use was 5.7 (IQR 4.8-6.3). Table 3 shows the factors that were associated with intention to use a Web-based program.

    Perceived ease of use, perceived usefulness, and number of children enrolled had P values less than .1 in the univariate analyses (see Table 3) and were included in the multivariable logistic regression. In the final model, only perceived ease of use (odds ratio [OR] 3.9, 95% CI 1.6-9.2, P=.002) and perceived usefulness (OR 28, 95% CI 8.0-95.2, P<.001) were significantly associated with a score of more than or equal to 6 for intention to use.

    Table 3. Factors associated with intention to use a Web-based program to support staff with implementing healthy eating and physical activity policies and practices (n=210).
     FactorsIntention to useaUnivariate analysis
     
    		Mean score of 1.0-5.9
    (n=59), n (%)    		Mean score of 6.0-7.0
    (n=151), n (%)    		X21P
    Locality (n=200)b

    		1.2.30

    		Urban53/56 (95)130/144 (90.3)


    		Rural3/56 (5)14/144 (9.7)

    Type of childcare center

    		1.2.30

    		Preschool25 (42)51 (33.8)


    		Nonpreschool34 (58)100 (66.2)

    Use of CCMScsoftware

    		2.4.10

    		Yes40 (68)118 (78.1)


    		No/don\'t know19 (32)33 (21.9)

    Number of children enrolled

    		3.5.06

    		75 or less27 (46)90 (59.6)


    		>7533 (54)60 (39.7)

    Number of staff members

    		1.4.20

    		1 to 832 (54)96 (63.6)


    		>827 (46)55 (36.4)

    Disadvantage (n=201)b

    		2.6.11

    		Low SESd17/56 (30)62/145 (42.8)


    		High SES39/56 (70)83/145 (57.2)

    Perceived ease of usee

    		21.4<.001

    		1.0-5.950 (85)74 (49.0)


    		6.0-7.09 (15)77 (51.0)

    Perceived usefulnesse

    		53.1<.001

    		1.0-5.956 (95)58 (38.4)


    		6.0-7.03 (5)93 (61.6)

    aScore of 1.0-5.9 indicates response to statements of strongly disagree to slightly agree, and score of 6.0-7.0 indicates response to statements of agree and strongly agree.

    bCenter number is less than the total as no Socio-Economic Indexes For Areas (SEIFA) and Accessibility/Remoteness Index of Australia (ARIA) score matched the center postcode.

    cChild Care Management System (CCMS).

    dSocioeconomic status (SES).

    eSignificant variables in multivariable model, P<.05.

    Features to Support Healthy Eating and Physical Activity Policy and Practice Implementation

    More than 90% of service managers reported the following features to be useful/very useful in supporting the implementation of healthy eating and physical activity policies and practices: decision-support systems to help support staff with planning a healthy menu (117/129, 90.7%), having links to useful nutrition and physical activity resources (212/212, 100%), updated information on nutrition and physical activity guidelines (208/212, 98.1%), and a having a feature which provided updated feedback on how their center was performing in relation to other centers (212/212, 100%) (see Table 4).

    Table 4. Features of a Web-based program reported by service managers as useful or very useful to help their staff implement healthy eating and physical activity policies and practices (n=212).
    Features of Web-based programn (%)95% CI
    Performance monitoring and feedback


    		Allows staff to input nutrition and physical activity information and monitor service’s progress over time.187 (88.2)83.9-92.5

    		Provides updated information on how your center is performing in relation to other centers.212 (100)99.3-100

    		Provides staff with tailored feedback based on your service’s needs, and suggested strategies to implement.198 (93.4)90.1-96.7

    		Allows staff to prioritize which nutrition or physical activity practice they would like to work on.201 (94.8)91.8-97.8
    Interaction and communication tools, prompts


    		Allows staff to ask a member of their local health promotion team for advice.208 (98.1)96.2-100

    		Allows staff to communicate with staff from other childcare centers via online chat rooms, discussion boards, or blogs.165 (77.8)72.2-83.4

    		Allows staff to communicate with parents about physical activity and nutrition or via online chat rooms, discussion boards, or blogs.143 (67.5)61.5-73.7

    		Reminds or prompts staff to deliver a physical activity or nutrition session, based on your service\'s daily schedule.182 (85.8)81.2-90.5
    Provision of education materials


    		Uses videos or interactive activities, including games, to demonstrate an activity.194 (91.5)87.8-95.3

    		Includes a database of healthy lunchbox options, which is regularly updated (n=116)a. 113 (97.5)94.5-100

    		Provides links to useful physical activity and nutrition resources.212 (100)99.3-100

    		Provides updated information on nutrition and physical activity guidelines relevant to preschools.208 (98.1)96.3-100
    Decision-support systems


    		Supports you and your staff in planning a healthy menu for your childcare center (n=129)b.117 (90.7)85.7-95.7

    aOnly services that required parents to provide food for their children answered this question.

    bOnly services that provided food for children answered this question.


    Principal Findings

    Almost all childcare center managers had access to computers and the Internet, with 40.2% (86/214) of centers also reporting having access to portable tablets. The majority of service managers reported high behavioral intention to use an electronic Web-based program to support their service with implementing healthy eating and physical activity-promoting policies and practices—71.9% (151/210) of service managers scored an average of 6 or more on intention to use. Constructs within the TAM—perceived ease of use and perceived usefulness—were significantly associated with intention to use. Several preferred features, including the capacity to provide feedback on how the service was performing, providing updated links to physical activity and nutrition resources, and use of decision-support systems to assist with planning menus, were consistently rated as useful or very useful to support practice improvement in this setting. The universal access that childcare center managers have to computers, the high proportion reporting intending to use such systems, and high acceptability of Web-based features suggest that there is considerable potential for electronic programs to be developed to support childcare center staff with implementation of healthy eating and physical activity-promoting policies and practices.

    Comparisons With Prior Work

    The near universal access that childcare center managers have to computers and the Internet is not surprising given the introduction of mandatory online recording systems for childcare benefit-approved services in Australia since 2009 [24] . The potential of newer forms of computer technology such as tablets to provide implementation support may increase the appeal of Web-based support programs given their portability, capacity to provide tailored interactive information in multiple formats, ease of navigation, and potential effectiveness in modifying other health behaviors [49].

    Consistent with previous research using TAM [40] and other research assessing characteristics associated with uptake of electronic interventions [50], perceived ease of use and perceived usefulness were significantly associated with service managers’ intention to use a Web-based program. Intention to use scores reported in this study are higher than those documented among students [33] and clinicians [40]. While these samples are not directly comparable, such findings suggest greater intention to use electronic Web-based programs among childcare center managers and are encouraging, given findings that behavioral intention to use scores, as measured by TAM, are associated with actual use of the program [44,45]. TAM may be a useful model to inform the design, implementation, and evaluation of electronic Web-based programs in childcare centers.

    The provision of training and educational materials, including guidelines and updated links to healthy eating and physical activity resources, were rated as useful by almost all service managers, a finding consistent with other studies which report that childcare center staff find training and resources useful to support their delivery of healthy eating and physical activity policies and practices [51,52]. Communications features which allowed staff to interact with parents and staff from other childcare centers were the least preferred function. This may reflect a preference for more conventional methods—face-to-face, telephone, or one-on-one communication methods—rather than chat rooms or forums as examined in this study. Previous studies in childcare centers have reported using Web-based resources largely for dissemination of information [23,53]. However, these findings suggest that an opportunity exists to use more interactive training resources and decision-support tools to support childcare centers with implementing healthy eating and physical activity-supporting policies and practices.

    Implications

    To ensure that the design characteristics of Web-based programs are both functional and easy to use, end-user engagement and feedback on the utility of such interventions need to be undertaken prior to introduction of new electronic interventions [41]. Further examination of the specific design characteristics that are associated with ease of use and perceived usefulness is needed to inform the development of Web-based programs that are most likely to be adopted by childcare center staff. Strategies to increase usefulness, such as incorporating features within the program that deal with tasks currently performed with computers or tablets (eg, reporting or administrative tasks) and engaging end users in development and pilot-testing of the program, could potentially be useful in facilitating uptake [41,45]. Research with clinicians suggest that the provision of staff training, establishing organizational support, and encouraging peer uptake and support may be useful to facilitate uptake of electronic interventions [41,45]. Further descriptive and intervention research examining the association between TAM constructs—perceived ease of use and perceived usefulness—and actual use of an electronic, Web-based program in childcare centers is needed.

    Strengths and Limitations

    Strengths of the study include the use of a large, randomly selected sample of centers from across an Australian state and the adaptation of a previously validated tool to assess intention to use a Web-based program. To our knowledge, this is the first study to describe childcare centers’ access to Web-based devices and factors associated with childcare service managers’ intention to use a Web-based program internationally. Nonetheless, a number of study limitations warrant mention. This study assessed intention to use, rather than actual use of electronic Web-based programs. While there is empirical evidence supporting the relationship between intention and actual use [44], and assessments of intention to use provide important formative information for program development [41], rates of actual use are likely to differ from those reported here. Childcare centers within a particular region of NSW—approximately 10% of centers—were also excluded due to participation in another trial [37]. A comparison between excluded centers (n=26) and those in this study found no significant difference in the number of usual providers (P=.22) and number of children enrolled (P=.88). A significantly larger proportion of centers located in this region were located in lower SES areas (P=.003) and rural areas (P=.015). As such, findings reported in this study are only likely to be representative of the 90% of centers in NSW from where the centers were randomly sampled. The survey was conducted with service managers of childcare centers who are likely to be involved in overseeing and coordinating the introduction of electronic Web-based interventions in their centers. Future assessments with childcare staff who may be primarily involved in the delivery of healthy eating and physical activity practices are likely to provide useful complementary information on how best to implement such programs in childcare centers. Notwithstanding these limitations, the study provides support for the potential of Web-based technology to make a significant contribution to the translation of evidence-based obesity prevention interventions in this setting.

    Conclusions

    Findings reported in this paper highlight the potential for electronic Web-based interventions to be used to support the implementation of healthy eating and physical activity-promoting policies and practices in childcare centers. Further research examining the development and effectiveness of using such modalities to support practice change within childcare centers is warranted to realize the potential of childcare centers for obesity prevention in the community.

    Acknowledgments

    Dr Luke Wolfenden is supported by a National Health and Medical Research Council (NHMRC) Career Development Fellowship. Infrastructure support was provided by the University of Newcastle, Hunter Medical Research Institute (HMRI) and Hunter New England Population Health (HNEPH). The authors wish to acknowledge Christophe Lecathelinais for his assistance with data analysis. This study received funding from the University of Newcastle Priority Research Centre for Health Behaviour pilot funding.

    Conflicts of Interest

    None declared.

    ‎

    Multimedia Appendix 1

    Modified TAM questionnaire (administered via computer-assisted telephone interview).

    PDF File (Adobe PDF File), 103KB
    1. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012 Dec 15;380(9859):2224-2260 [FREE Full text] [CrossRef] [Medline]
    2. Stevens GA, Singh GM, Lu Y, Danaei G, Lin JK, Finucane MM, Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Body Mass Index). National, regional, and global trends in adult overweight and obesity prevalences. Popul Health Metr 2012;10(1):22 [FREE Full text] [CrossRef] [Medline]
    3. de Onis M, Blössner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr 2010 Nov;92(5):1257-1264 [FREE Full text] [CrossRef] [Medline]
    4. Must A. Does overweight in childhood have an impact on adult health? Nutr Rev 2003 Apr;61(4):139-142. [Medline]
    5. Hesketh KD, Campbell KJ. Interventions to prevent obesity in 0-5 year olds: an updated systematic review of the literature. Obesity (Silver Spring) 2010 Feb;18 Suppl 1:S27-S35. [CrossRef] [Medline]
    6. Birch LL, Ventura AK. Preventing childhood obesity: what works? Int J Obes (Lond) 2009 Apr;33 Suppl 1:S74-S81. [CrossRef] [Medline]
    7. Childhood Education and Care, Australia, June 2011. Canberra, Australia: Australian Bureau of Statistics   URL: http:/​/www.​abs.gov.au/​ausstats/​abs@.nsf/​Products/​4402.​0~June+2011~Main+Features~Main+features?OpenDocument [accessed 2014-06-26] [WebCite Cache]
    8. Bolstad R. New Zealand Council for Educational Research. Wellington, New Zealand: Ministry of Education; 2004 Nov. The role and potential of ICT in early childhood education: A review of New Zealand and international literature   URL: http://www.nzcer.org.nz/system/files/ictinecefinal.pdf [accessed 2014-06-26] [WebCite Cache]
    9. Get Up and Grow: Healthy Eating and Physical Activity for Early Childhood. Canberra, Australia: Commonwealth of Australia; 2009.   URL: http:/​/www.​health.gov.au/​internet/​main/​publishing.nsf/​Content/​EA1E1000D846F0AFCA257BF0001DADB3/​$File/​HEPA%20-%20A4%20Book%20-%20Directors%20Book%20-%20LR.​pdf [accessed 2014-06-17] [WebCite Cache]
    10. Committee on Accelerating Progress in Obesity Prevention, Food and Nutrition Board, Institute of Medicine. Accelerating Progress in Obesity Prevention: Solving the Weight of the Nation. Washington, DC: The National Academies Press; 2012.   URL: http:/​/extension.​oregonstate.edu/​physicalactivity/​sites/​default/​files/​accelerating_progress_in_obesity_prevention.​pdf [accessed 2014-06-26] [WebCite Cache]
    11. Larson N, Ward DS, Neelon SB, Story M. What role can child-care settings play in obesity prevention? A review of the evidence and call for research efforts. J Am Diet Assoc 2011 Sep;111(9):1343-1362. [CrossRef] [Medline]
    12. Bluford DA, Sherry B, Scanlon KS. Interventions to prevent or treat obesity in preschool children: a review of evaluated programs. Obesity (Silver Spring) 2007 Jun;15(6):1356-1372. [CrossRef] [Medline]
    13. Rogers EM. Diffusion of preventive innovations. Addict Behav 2002;27(6):989-993. [Medline]
    14. McWilliams C, Ball SC, Benjamin SE, Hales D, Vaughn A, Ward DS. Best-practice guidelines for physical activity at child care. Pediatrics 2009 Dec;124(6):1650-1659 [FREE Full text] [CrossRef] [Medline]
    15. Wolfenden L, Neve M, Farrell L, Lecathelinais C, Bell C, Milat A, et al. Physical activity policies and practices of childcare centres in Australia. J Paediatr Child Health 2011 Mar;47(3):73-76. [CrossRef] [Medline]
    16. de Silva-Sanigorski AM, Bell AC, Kremer P, Nichols M, Crellin M, Smith M, et al. Reducing obesity in early childhood: results from Romp & Chomp, an Australian community-wide intervention program. Am J Clin Nutr 2010 Apr;91(4):831-840 [FREE Full text] [CrossRef] [Medline]
    17. Finch M, Wolfenden L, Falkiner M, Edenden D, Pond N, Hardy LL, et al. Impact of a population based intervention to increase the adoption of multiple physical activity practices in centre based childcare services: a quasi experimental, effectiveness study. Int J Behav Nutr Phys Act 2012;9:101 [FREE Full text] [CrossRef] [Medline]
    18. Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res 2010;12(1):e4 [FREE Full text] [CrossRef] [Medline]
    19. Gosliner WA, James P, Yancey AK, Ritchie L, Studer N, Crawford PB. Impact of a worksite wellness program on the nutrition and physical activity environment of child care centers. Am J Health Promot 2010;24(3):186-189. [CrossRef] [Medline]
    20. 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 [FREE Full text] [CrossRef] [Medline]
    21. Lyn R, Maalouf J, Evers S, Davis J, Griffin M. Nutrition and physical activity in child care centers: the impact of a wellness policy initiative on environment and policy assessment and observation outcomes, 2011. Prev Chronic Dis 2013;10:E83 [FREE Full text] [CrossRef] [Medline]
    22. Milat AJ, King L, Bauman AE, Redman S. The concept of scalability: increasing the scale and potential adoption of health promotion interventions into policy and practice. Health Promot Int 2013 Sep;28(3):285-298 [FREE Full text] [CrossRef] [Medline]
    23. Dockett S, Perry B, Nanlohy P. Computers in early childhood services: a part of the educational program or less time for play? Aust Res Early Child Educ 1999;6(2):165-176.
    24. Australian Government Department of Education. Child care management system   URL: http://education.gov.au/child-care-management-system [accessed 2014-06-17] [WebCite Cache]
    25. Rabin BA, Glasgow RE, Kerner JF, Klump MP, Brownson RC. Dissemination and implementation research on community-based cancer prevention: a systematic review. Am J Prev Med 2010 Apr;38(4):443-456. [CrossRef] [Medline]
    26. Gilbody S, Whitty P, Grimshaw J, Thomas R. Educational and organizational interventions to improve the management of depression in primary care: a systematic review. JAMA 2003 Jun 18;289(23):3145-3451. [CrossRef] [Medline]
    27. Balas EA, Weingarten S, Garb CT, Blumenthal D, Boren SA, Brown GD. Improving preventive care by prompting physicians. Arch Intern Med 2000 Feb 14;160(3):301-308. [Medline]
    28. Robertson A, Cresswell K, Takian A, Petrakaki D, Crowe S, Cornford T, et al. Implementation and adoption of nationwide electronic health records in secondary care in England: qualitative analysis of interim results from a prospective national evaluation. BMJ 2010;341:c4564 [FREE Full text] [Medline]
    29. Moxey A, Robertson J, Newby D, Hains I, Williamson M, Pearson SA. Computerized clinical decision support for prescribing: provision does not guarantee uptake. J Am Med Inform Assoc 2010;17(1):25-33 [FREE Full text] [CrossRef] [Medline]
    30. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci 2009;4:50 [FREE Full text] [CrossRef] [Medline]
    31. Johnson KB. Barriers that impede the adoption of pediatric information technology. Arch Pediatr Adolesc Med 2001 Dec;155(12):1374-1379. [Medline]
    32. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: a comparison of two theoretical models. Manage Sci 1989 Aug;35(8):982-1003. [CrossRef]
    33. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Quarterly 2003 Sep;27:425-478.
    34. Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O. Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q 2004;82(4):581-629 [FREE Full text] [CrossRef] [Medline]
    35. Centre for Epidemiology and Evidence. 2009-2010 Summary Report From the New South Wales Child Health Survey. Sydney, Australia: NSW Ministry of Health; 2012.   URL: http://www.health.nsw.gov.au/surveys/child/Publications/hsc_0910.pdf [accessed 2014-06-26] [WebCite Cache]
    36. Preschool Education, Australia, 2013. Canberra, Australia: Australian Bureau of Statistics; 2014.   URL: http:/​/www.​abs.gov.au/​ausstats/​abs@.nsf/​Products/​4240.​0~2013~Main+Features~Main+Features?OpenDocument [accessed 2014-06-17] [WebCite Cache]
    37. Jones J, Wolfenden L, Wyse R, Finch M, Yoong SL, Dodds P, et al. A randomised controlled trial of an intervention to facilitate the implementation of healthy eating and physical activity policies and practices in childcare services. BMJ Open 2014;4(4):e005312 [FREE Full text] [CrossRef] [Medline]
    38. Guide to the National Quality Standard. Sydney, Australia: Australian Children's Educaton & Care Quality Authority; 2013 Sep.   URL: http:/​/files.​acecqa.gov.au/​files/​National-Quality-Framework-Resources-Kit/​NQF03-Guide-to-NQS-130902.​pdf [accessed 2014-06-17] [WebCite Cache]
    39. King WR, He J. A meta-analysis of the technology acceptance model. Information & Management 2006 Sep;43(6):740-755. [CrossRef]
    40. Holden RJ, Karsh BT. The technology acceptance model: its past and its future in health care. J Biomed Inform 2010 Feb;43(1):159-172 [FREE Full text] [CrossRef] [Medline]
    41. Venkatesh V, Bala H. Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences 2008 May;39(2):273-315. [CrossRef]
    42. Mao E, Palvia P. Testing an extended model of IT acceptance in the Chinese cultural context. SIGMIS Database 2006 Sep 19;37(2-3):20. [CrossRef]
    43. Straub D, Keil M, Brenner W. Testing the technology acceptance model across cultures: A three country study. Information & Management 1997 Nov;33(1):1-11. [CrossRef]
    44. Turner M, Kitchenham B, Brereton P, Charters S, Budgen D. Does the technology acceptance model predict actual use? A systematic literature review. Inf Softw Technol 2010 May;52(5):463-479. [CrossRef]
    45. Legris P, Ingham J, Collerette P. Why do people use information technology? A critical review of the technology acceptance model. Information & Management 2003 Jan;40(3):191-204. [CrossRef]
    46. Baulch J, Chester A, Brennan L. Adolescent and parent content preferences and predictors of intention to use an online healthy weight website for adolescents. E-Journal Appl Psychol 2010;6(1):19-27.
    47. Grimshaw JM, Shirran L, Thomas R, Mowatt G, Fraser C, Bero L, et al. Changing provider behavior: an overview of systematic reviews of interventions. Med Care 2001 Aug;39(8 Suppl 2):II2-II45. [Medline]
    48. Abraham C, Michie S. A taxonomy of behavior change techniques used in interventions. Health Psychol 2008 May;27(3):379-387. [CrossRef] [Medline]
    49. Free C, Phillips G, Galli L, Watson L, Felix L, Edwards P, et al. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med 2013;10(1):e1001362 [FREE Full text] [CrossRef] [Medline]
    50. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005 Apr 2;330(7494):765 [FREE Full text] [CrossRef] [Medline]
    51. Pollard CM, Lewis JM, Miller MR. Food service in long day care centres--an opportunity for public health intervention. Aust N Z J Public Health 1999 Dec;23(6):606-610. [Medline]
    52. Kim J, Shim JE, Wiley AR, Kim K, McBride BA. Is there a difference between center and home care providers' training, perceptions, and practices related to obesity prevention? Matern Child Health J 2012 Nov;16(8):1559-1566. [CrossRef] [Medline]
    53. Buller DB, Buller MK, Kane I. Web-based strategies to disseminate a sun safety curriculum to public elementary schools and state-licensed child-care facilities. Health Psychol 2005 Sep;24(5):470-476. [CrossRef] [Medline]

    ‎
    ARIA: Accessibility/Remoteness Index of Australia
    CATI: computer-assisted telephone interview
    CCMS: Child Care Management System
    HMRI: Hunter Medical Research Institute
    HNELHD: Hunter New England Local Health District
    HNEPH: Hunter New England Population Health
    IQR: interquartile range
    NHMRC: National Health and Medical Research Council
    NSW: New South Wales
    OR: odds ratio
    SEIFA: Socio-Economic Indexes For Areas
    TAM: Technology Acceptance Model

    Edited by G Eysenbach; submitted 26.06.14; peer-reviewed by K Leverentz-Brady, M Ho; comments to author 25.11.14; revised version received 29.12.14; accepted 04.02.15; published 30.04.15

    Copyright

    ©Sze Lin Yoong, Christopher Michael Williams, Meghan Finch, Rebecca Wyse, Jannah Jones, Megan Freund, John Henry Wiggers, Nicole Nathan, Pennie Dodds, Luke Wolfenden. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 30.04.2015.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.