A rapid review of the scalability of interventions targeting obesity prevention in infants.

ISSUE ADDRESSED: Obesity in children is one of the most significant public health issues globally. Given the high prevalence of overweight and obesity in children, there is a need to identify effective obesity prevention efforts that can be delivered at scale to improve child health. We aimed to (i) identify obesity prevention interventions targeted at children aged 0-2 that have been scaled-up, and their relative efficacy compared to their pre-scale trial, (ii) describe adaptations made, and the extent to which factors related to scalability have been reported. METHODS: We conducted a rapid review of pre-scale randomised controlled trials targeting nutrition, physical activity and obesity prevention in infants, and calculated the relative effect size for relevant outcomes in the corresponding scaled up trial. We documented adaptations made to the pre-scale trial for scale up, and explored how different components of scalability had been reported according to the Intervention Scalability Assessment Tool. RESULTS: Of the 14 identified pre-scale trials, only one formal evaluation of the scale-up trial was identified. For body mass index, <10% of the effect was retained, however for nutrition and behavioural outcomes, the proportion of effect retained varied from -11.1% to 144%. Significant adaptations to modality were made in the scaled up trial primarily to reduce cost and increase reach of the intervention. Reporting of scalability components varied across the 14 trials, with only one trial reporting information for all assessed components. CONCLUSIONS: The majority of effective interventions targeting obesity prevention in infants have not been evaluated in a scaled up form. The magnitude of effect retained for the single trial that was scaled up was variable. In general, reporting of components of scalability was sub-optimal. SO WHAT?: The findings suggest that there is substantial need for the development and rigorous evaluation of obesity prevention interventions in children aged 0-2 which are amenable for scale.

Healthy eating interventions delivered in early childhood education and care settings for improving the diet of children aged six months to six years.

BACKGROUND: Dietary intake during early childhood can have implications on child health and developmental trajectories. Early childhood education and care (ECEC) services are recommended settings to deliver healthy eating interventions as they provide access to many children during this important period. Healthy eating interventions delivered in ECEC settings can include strategies targeting the curriculum (e.g. nutrition education), ethos and environment (e.g. menu modification) and partnerships (e.g. workshops for families). Despite guidelines supporting the delivery of healthy eating interventions in this setting, little is known about their impact on child health. OBJECTIVES: To assess the effectiveness of healthy eating interventions delivered in ECEC settings for improving dietary intake in children aged six months to six years, relative to usual care, no intervention or an alternative, non-dietary intervention. Secondary objectives were to assess the impact of ECEC-based healthy eating interventions on physical outcomes (e.g. child body mass index (BMI), weight, waist circumference), language and cognitive outcomes, social/emotional and quality-of-life outcomes. We also report on cost and adverse consequences of ECEC-based healthy eating interventions. SEARCH METHODS: We searched eight electronic databases including CENTRAL, MEDLINE, Embase, CINAHL, PsycINFO, ERIC, Scopus and SportDiscus on 24 February 2022. We searched reference lists of included studies, reference lists of relevant systematic reviews, the World Health Organization International Clinical Trials Registry Platform, ClinicalTrials.gov and Google Scholar, and contacted authors of relevant papers. SELECTION CRITERIA: We included randomised controlled trials (RCTs), including cluster-RCTs, stepped-wedge RCTs, factorial RCTs, multiple baseline RCTs and randomised cross-over trials, of healthy eating interventions targeting children aged six months to six years that were conducted within the ECEC setting. ECEC settings included preschools, nurseries, kindergartens, long day care and family day care. To be included, studies had to include at least one intervention component targeting child diet within the ECEC setting and measure child dietary or physical outcomes, or both. DATA COLLECTION AND ANALYSIS: Pairs of review authors independently screened titles and abstracts and extracted study data. We assessed risk of bias for all studies against 12 criteria within RoB 1, which allows for consideration of how selection, performance, attrition, publication and reporting biases impact outcomes. We resolved discrepancies via consensus or by consulting a third review author. Where we identified studies with suitable data and homogeneity, we performed meta-analyses using a random-effects model; otherwise, we described findings using vote-counting approaches and via harvest plots. For measures with similar metrics, we calculated mean differences (MDs) for continuous outcomes and risk ratios (RRs) for dichotomous outcomes. We calculated standardised mean differences (SMDs) for primary and secondary outcomes where studies used different measures. We applied GRADE to assess certainty of evidence for dietary, cost and adverse outcomes. MAIN RESULTS: We included 52 studies that investigated 58 interventions (described across 96 articles). All studies were cluster-RCTs. Twenty-nine studies were large (≥ 400 participants) and 23 were small (< 400 participants). Of the 58 interventions, 43 targeted curriculum, 56 targeted ethos and environment, and 50 targeted partnerships. Thirty-eight interventions incorporated all three components. For the primary outcomes (dietary outcomes), we assessed 19 studies as overall high risk of bias, with performance and detection bias being most commonly judged as high risk of bias. ECEC-based healthy eating interventions versus usual practice or no intervention may have a positive effect on child diet quality (SMD 0.34, 95% confidence interval (CI) 0.04 to 0.65; P = 0.03, I2 = 91%; 6 studies, 1973 children) but the evidence is very uncertain. There is moderate-certainty evidence that ECEC-based healthy eating interventions likely increase children's consumption of fruit (SMD 0.11, 95% CI 0.04 to 0.18; P < 0.01, I2 = 0%; 11 studies, 2901 children). The evidence is very uncertain about the effect of ECEC-based healthy eating interventions on children's consumption of vegetables (SMD 0.12, 95% CI -0.01 to 0.25; P =0.08, I2 = 70%; 13 studies, 3335 children). There is moderate-certainty evidence that ECEC-based healthy eating interventions likely result in little to no difference in children's consumption of non-core (i.e. less healthy/discretionary) foods (SMD -0.05, 95% CI -0.17 to 0.08; P = 0.48, I2 = 16%; 7 studies, 1369 children) or consumption of sugar-sweetened beverages (SMD -0.10, 95% CI -0.34 to 0.14; P = 0.41, I2 = 45%; 3 studies, 522 children). Thirty-six studies measured BMI, BMI z-score, weight, overweight and obesity, or waist circumference, or a combination of some or all of these. ECEC-based healthy eating interventions may result in little to no difference in child BMI (MD -0.08, 95% CI -0.23 to 0.07; P = 0.30, I2 = 65%; 15 studies, 3932 children) or in child BMI z-score (MD -0.03, 95% CI -0.09 to 0.03; P = 0.36, I2 = 0%; 17 studies; 4766 children). ECEC-based healthy eating interventions may decrease child weight (MD -0.23, 95% CI -0.49 to 0.03; P = 0.09, I2 = 0%; 9 studies, 2071 children) and risk of overweight and obesity (RR 0.81, 95% CI 0.65 to 1.01; P = 0.07, I2 = 0%; 5 studies, 1070 children). ECEC-based healthy eating interventions may be cost-effective but the evidence is very uncertain (6 studies). ECEC-based healthy eating interventions may have little to no effect on adverse consequences but the evidence is very uncertain (3 studies). Few studies measured language and cognitive skills (n = 2), social/emotional outcomes (n = 2) and quality of life (n = 3). AUTHORS' CONCLUSIONS: ECEC-based healthy eating interventions may improve child diet quality slightly, but the evidence is very uncertain, and likely increase child fruit consumption slightly. There is uncertainty about the effect of ECEC-based healthy eating interventions on vegetable consumption. ECEC-based healthy eating interventions may result in little to no difference in child consumption of non-core foods and sugar-sweetened beverages. Healthy eating interventions could have favourable effects on child weight and risk of overweight and obesity, although there was little to no difference in BMI and BMI z-scores. Future studies exploring the impact of specific intervention components, and describing cost-effectiveness and adverse outcomes are needed to better understand how to maximise the impact of ECEC-based healthy eating interventions.

Healthy eating interventions delivered in early childhood education and care settings for improving the diet of children aged six months to six years.

BACKGROUND: Dietary intake during early childhood can have implications on child health and developmental trajectories. Early childhood education and care (ECEC) services are recommended settings to deliver healthy eating interventions as they provide access to many children during this important period. Healthy eating interventions delivered in ECEC settings can include strategies targeting the curriculum (e.g. nutrition education), ethos and environment (e.g. menu modification) and partnerships (e.g. workshops for families). Despite guidelines supporting the delivery of healthy eating interventions in this setting, little is known about their impact on child health. OBJECTIVES: To assess the effectiveness of healthy eating interventions delivered in ECEC settings for improving dietary intake in children aged six months to six years, relative to usual care, no intervention or an alternative, non-dietary intervention. Secondary objectives were to assess the impact of ECEC-based healthy eating interventions on physical outcomes (e.g. child body mass index (BMI), weight, waist circumference), language and cognitive outcomes, social/emotional and quality-of-life outcomes. We also report on cost and adverse consequences of ECEC-based healthy eating interventions. SEARCH METHODS: We searched eight electronic databases including CENTRAL, MEDLINE, Embase, CINAHL, PsycINFO, ERIC, Scopus and SportDiscus on 24 February 2022. We searched reference lists of included studies, reference lists of relevant systematic reviews, the World Health Organization International Clinical Trials Registry Platform, ClinicalTrials.gov and Google Scholar, and contacted authors of relevant papers. SELECTION CRITERIA: We included randomised controlled trials (RCTs), including cluster-RCTs, stepped-wedge RCTs, factorial RCTs, multiple baseline RCTs and randomised cross-over trials, of healthy eating interventions targeting children aged six months to six years that were conducted within the ECEC setting. ECEC settings included preschools, nurseries, kindergartens, long day care and family day care. To be included, studies had to include at least one intervention component targeting child diet within the ECEC setting and measure child dietary or physical outcomes, or both. DATA COLLECTION AND ANALYSIS: Pairs of review authors independently screened titles and abstracts and extracted study data. We assessed risk of bias for all studies against 12 criteria within RoB 1, which allows for consideration of how selection, performance, attrition, publication and reporting biases impact outcomes. We resolved discrepancies via consensus or by consulting a third review author. Where we identified studies with suitable data and homogeneity, we performed meta-analyses using a random-effects model; otherwise, we described findings using vote-counting approaches and via harvest plots. For measures with similar metrics, we calculated mean differences (MDs) for continuous outcomes and risk ratios (RRs) for dichotomous outcomes. We calculated standardised mean differences (SMDs) for primary and secondary outcomes where studies used different measures. We applied GRADE to assess certainty of evidence for dietary, cost and adverse outcomes.  MAIN RESULTS: We included 52 studies that investigated 58 interventions (described across 96 articles). All studies were cluster-RCTs. Twenty-nine studies were large (≥ 400 participants) and 23 were small (< 400 participants). Of the 58 interventions, 43 targeted curriculum, 56 targeted ethos and environment, and 50 targeted partnerships. Thirty-eight interventions incorporated all three components. For the primary outcomes (dietary outcomes), we assessed 19 studies as overall high risk of bias, with performance and detection bias being most commonly judged as high risk of bias. ECEC-based healthy eating interventions versus usual practice or no intervention may have a positive effect on child diet quality (SMD 0.34, 95% confidence interval (CI) 0.04 to 0.65; P = 0.03, I2 = 91%; 6 studies, 1973 children) but the evidence is very uncertain. There is moderate-certainty evidence that ECEC-based healthy eating interventions likely increase children's consumption of fruit (SMD 0.11, 95% CI 0.04 to 0.18; P < 0.01, I2 = 0%; 11 studies, 2901 children). The evidence is very uncertain about the effect of ECEC-based healthy eating interventions on children's consumption of vegetables (SMD 0.12, 95% CI -0.01 to 0.25; P =0.08, I2 = 70%; 13 studies, 3335 children). There is moderate-certainty evidence that ECEC-based healthy eating interventions likely result in little to no difference in children's consumption of non-core (i.e. less healthy/discretionary) foods (SMD -0.05, 95% CI -0.17 to 0.08; P = 0.48, I2 = 16%; 7 studies, 1369 children) or consumption of sugar-sweetened beverages (SMD -0.10, 95% CI -0.34 to 0.14; P = 0.41, I2 = 45%; 3 studies, 522 children). Thirty-six studies measured BMI, BMI z-score, weight, overweight and obesity, or waist circumference, or a combination of some or all of these. ECEC-based healthy eating interventions may result in little to no difference in child BMI (MD -0.08, 95% CI -0.23 to 0.07; P = 0.30, I2 = 65%; 15 studies, 3932 children) or in child BMI z-score (MD -0.03, 95% CI -0.09 to 0.03; P = 0.36, I2 = 0%; 17 studies; 4766 children). ECEC-based healthy eating interventions may decrease child weight (MD -0.23, 95% CI -0.49 to 0.03; P = 0.09, I2 = 0%; 9 studies, 2071 children) and risk of overweight and obesity (RR 0.81, 95% CI 0.65 to 1.01; P = 0.07, I2 = 0%; 5 studies, 1070 children). ECEC-based healthy eating interventions may be cost-effective but the evidence is very uncertain (6 studies). ECEC-based healthy eating interventions may have little to no effect on adverse consequences but the evidence is very uncertain (3 studies). Few studies measured language and cognitive skills (n = 2), social/emotional outcomes (n = 2) and quality of life (n = 3). AUTHORS' CONCLUSIONS: ECEC-based healthy eating interventions may improve child diet quality slightly, but the evidence is very uncertain, and likely increase child fruit consumption slightly. There is uncertainty about the effect of ECEC-based healthy eating interventions on vegetable consumption. ECEC-based healthy eating interventions may result in little to no difference in child consumption of non-core foods and sugar-sweetened beverages. Healthy eating interventions could have favourable effects on child weight and risk of overweight and obesity, although there was little to no difference in BMI and BMI z-scores. Future studies exploring the impact of specific intervention components, and describing cost-effectiveness and adverse outcomes are needed to better understand how to maximise the impact of ECEC-based healthy eating interventions.

Assessing the scalability of healthy eating interventions within the early childhood education and care setting: secondary analysis of a Cochrane systematic review.

OBJECTIVE: Early childhood education and care (ECEC) is a recommended setting for the delivery of health eating interventions 'at scale' (i.e. to large numbers of childcare services) to improve child public health nutrition. Appraisal of the 'scalability' (suitability for delivery at scale) of interventions is recommended to guide public health decision-making. This study describes the extent to which factors required to assess scalability are reported among ECEC-based healthy eating interventions. DESIGN: Studies from a recent Cochrane systematic review assessing the effectiveness of healthy eating interventions delivered in ECEC for improving child dietary intake were included. The reporting of factors of scalability was assessed against domains outlined within the Intervention Scalability Assessment Tool (ISAT). The tool recommends decision makers consider the problem, the intervention, strategic and political context, effectiveness, costs, fidelity and adaptation, reach and acceptability, delivery setting and workforce, implementation infrastructure and sustainability. Data were extracted by one reviewer and checked by a second reviewer. SETTING: ECEC. PARTICIPANTS: Children 6 months to 6 years. RESULTS: Of thirty-eight included studies, none reported all factors within the ISAT. All studies reported the problem, the intervention, effectiveness and the delivery workforce and setting. The lowest reported domains were intervention costs (13 % of studies) and sustainability (16 % of studies). CONCLUSIONS: Findings indicate there is a lack of reporting of some key factors of scalability for ECEC-based healthy eating interventions. Future studies should measure and report such factors to support policy and practice decision makers when selecting interventions to be scaled-up.

Describing the evidence-base for research engagement by health care providers and health care organisations: a scoping review.

BACKGROUND: Having a research-engaged health and medical workforce is associated with improvements in clinical outcomes for patients. As such, there has been significant government investment internationally to support health care organisations and services to increase staff engagement with research. OBJECTIVES: This scoping review sought to provide an overview of the literature describing strategies employed to increase research engagement by health care providers and organisations, and to undertake a qualitative analysis to generate a list of research engagement strategies. METHODS: A scoping review using systematic search strategies was undertaken to locate peer-review publications and grey literature related to research engagement by health care providers and organisations. Research engagement was defined as a 'deliberate set of intellectual and practical activities undertaken by health care staff and organisations to conduct research'. A database search of electronic records was performed with no limit on publication date. Publications were included regardless of study type (excluding systematic reviews) and categorised as either databased (presenting data or new analysis of existing data) and non-databased (no new data or analyses). Databased publications were further classified according to study type, study design and setting. A qualitative synthesis using a Framework Approach was undertaken with all studies that described a strategy to improve research engagement. RESULTS: A total of 152 publications were included in this study with 54% categorised as non-databased. Of the databased articles, the majority (72%) were descriptive studies describing prevalence of correlates of research engagement, 17 (25%) described intervention studies where only two were controlled studies. The following research engagement strategies were identified: i) dual skilled team/staff, ii) resources or physical infrastructure, iii) incentives, iv) leadership support of research, v) education/training, vi) networks, vii) forming partnerships or collaborations and viii) overall leadership structure of entity. CONCLUSIONS: The literature on research engagement is primarily opinion-based and descriptive in nature. To provide the evidence needed to inform strategies, this needs to progress beyond descriptive to more rigorous well-designed intervention research.

The Effectiveness of Strategies to Improve User Engagement With Digital Health Interventions Targeting Nutrition, Physical Activity, and Overweight and Obesity: Systematic Review and Meta-Analysis.

BACKGROUND: Digital health interventions (DHIs) are effective in improving poor nutrition, physical inactivity, overweight and obesity. There is evidence suggesting that the impact of DHIs may be enhanced by improving user engagement. However, little is known about the overall effectiveness of strategies on engagement with DHIs. OBJECTIVE: This study aims to assess the overall effectiveness of strategies to improve engagement with DHIs targeting nutrition, physical activity, and overweight or obesity and explore associations between strategies and engagement outcomes. The secondary aim was to explore the impact of these strategies on health risk outcomes. METHODS: The MEDLINE, Embase, PsycINFO, CINAHL, CENTRAL, Scopus, and Academic Source Complete databases were searched up to July 24, 2023. Eligible studies were randomized controlled trials that evaluated strategies to improve engagement with DHIs and reported on outcomes related to DHI engagement (use or user experience). Strategies were classified according to behavior change techniques (BCTs) and design features (eg, supplementary emails). Multiple-variable meta-analyses of the primary outcomes (usage and user experience) were undertaken to assess the overall effectiveness of strategies. Meta-regressions were conducted to assess associations between strategies and use and user experience outcomes. Synthesis of secondary outcomes followed the "Synthesis Without Meta-Analysis" guidelines. The methodological quality and evidence was assessed using the Cochrane risk-of-bias tool, and the Grading of Recommendations Assessment, Development, and Evaluation tool respectively. RESULTS: Overall, 54 studies (across 62 publications) were included. Pooled analysis found very low-certainty evidence of a small-to-moderate positive effect of the use of strategies to improve DHI use (standardized mean difference=0.33, 95% CI 0.20-0.46; P<.001) and very low-certainty evidence of a small-to-moderate positive effect on user experience (standardized mean difference=0.29, 95% CI 0.07-0.52; P=.01). A significant positive association was found between the BCTs social support (effect size [ES]=0.40, 95% CI 0.14-0.66; P<.001) and shaping knowledge (ES=0.39, 95% CI 0.03-0.74; P=.03) and DHI use. A significant positive association was found among the BCTs social support (ES=0.70, 95% CI 0.18-1.22; P=.01), repetition and substitution (ES=0.29, 95% CI 0.05-0.53; P=.03), and natural consequences (ES=0.29, 95% CI 0.05-0.53; P=.02); the design features email (ES=0.29, 95% CI 0.05-0.53; P=.02) and SMS text messages (ES=0.34, 95% CI 0.11-0.57; P=.01); and DHI user experience. For secondary outcomes, 47% (7/15) of nutrition-related, 73% (24/33) of physical activity-related, and 41% (14/34) of overweight- and obesity-related outcomes reported an improvement in health outcomes. CONCLUSIONS: Although findings suggest that the use of strategies may improve engagement with DHIs targeting such health outcomes, the true effect is unknown because of the low quality of evidence. Future research exploring whether specific forms of social support, repetition and substitution, natural consequences, emails, and SMS text messages have a greater impact on DHI engagement is warranted. TRIAL REGISTRATION: PROSPERO CRD42018077333; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=77333.

Parent-reported compared with researcher-measured child height and weight: impact on body mass index classification in Australian pre-school aged children.

ISSUE ADDRESSED: Parent-reported data may provide a practical and cheap way for estimating young children's weight status. This study aims to compare the validity and reliability of parent-reported height and weight to researcher-measured data for pre-school aged children (aged 2-6 years). METHODS: This was a nested study within a cluster randomised controlled trial (October 2016-April 2017), conducted within 32 Early Childhood Education and Care (ECEC) services across New South Wales, Australia. Parents of children reported on demographics and child height and weight via a survey. For the same child, height and weight data were objectively collected by trained research staff at the service. We calculated mean differences, intra-class correlations, Bland-Altman plots, percentage agreement and Cohen's kappa coefficient (>0.8 = "excellent"; 0.61-0.8 = "good"; 0.41-0.60 = "moderate"; 0.21 and 0.4 = "fair [weak]"; <0.2 = "poor"). RESULTS: Overall, 89 children were included (mean age: 4.7 years; 59.5% female). The mean difference between parent-reported and researcher-measured data were small (BMI z-score: mean difference -0.01 [95% CI: -0.45 to 0.44]). There was "fair/weak" agreement between parent-categorised child BMI compared with researcher-measured data (Cohen's Kappa 0.24 [95% CI: 0.06 to 0.42]). Agreement was poor (Cohen's kappa <0.2) for female children, when reported by fathers or by parents with a BMI > 25 kg/m2 . CONCLUSION: There was "fair/weak" agreement between parent-reported and measured estimates of child weight status. SO WHAT?: Parent's report of weight and height may be a weak indicator of adiposity at the level of individuals however it may be useful for aggregate estimates.

Dissemination of public health research evidence and guidelines to Australian Early Childhood Education and Care staff: Views about source, content and format.

ISSUE ADDRESSED: Effective dissemination of public health research and evidence-based guidelines to Early Childhood Education and Care (ECEC) staff is critical for promoting research transfer and uptake and achieving positive outcomes for children. METHODS: A cross-sectional study was conducted during August 2021 to March 2022, with a sub-sample of Australian ECEC services participating in a larger survey completed online and via Computer-Assisted Telephone Interview. Survey items assessed: influential source for receiving research, type of content that would influence decisions to adopt research and preferred formats for receiving research. RESULTS: Overall, 993 service managers or staff from 1984 (50.0%) invited and eligible services completed the larger survey. Of these, 463 randomly allocated services (46.7%) had staff complete the dissemination items. The Australian Children's Education and Care Quality Authority, ECEC agencies and Government Departments were most frequently selected as influential sources of research evidence. Staff were most interested in content providing evidence-based recommendations for future actions and descriptions of health issues addressed. Workshops or conferences and webinars were the preferred format for receiving research. CONCLUSIONS: Findings highlight the importance of tailoring dissemination strategies to meet ECEC staff needs and engaging influential sources to disseminate research evidence. SO WHAT?: Understanding dissemination preferences of ECEC staff is crucial for supporting uptake of evidence-based health promotion in this setting. By developing tailored strategies based on ECEC preferences, research transfer and evidence-based decision making can be supported more effectively. These findings contribute to bridging the evidence-practice gap and improving the quality of care and health outcomes for children in ECEC settings.

Barriers and facilitators to the implementation of healthy eating, physical activity and obesity prevention policies, practices or programs in family day care: A mixed method systematic review.

Family day care (FDC) services provide care to young children typically within the carer's own home and represents a unique setting to deliver public health programs to improve child health. To support the implementation of programs targeting healthy eating, physical activity and obesity prevention in the FDC setting, an understanding of the factors influencing their implementation is required. This mixed methods systematic review aimed to describe the barriers and facilitators to the implementation of healthy eating, physical activity or obesity prevention policies, practices and programs (hereafter referred to as programs) in the FDC setting, and synthesise these according to the Theoretical Domains Framework (TDF). Electronic searches were conducted in 7 databases up to July 2020 to identify studies reporting the barriers and/or facilitators to program implementation in the FDC setting. Methodological quality assessments of included studies were conducted using the Mixed Methods Appraisal Tool (MMAT). Twenty studies met the review inclusion criteria (12 qualitative, 6 quantitative, 2 mixed methods). Of the 20 included studies, 16 reported barriers and facilitators mapped to the 'environmental contexts and resources' TDF domain; 10 reported barriers mapped to the 'social influences' TDF domain. Ten of the 12 qualitative studies and none of the quantitative or mixed method studies met all relevant MMAT criteria. This review comprehensively describes barriers and facilitators that need to be addressed to improve the implementation of healthy eating, physical activity and obesity prevention programs in FDC to ensure the expected health benefits of such programs reach children attending FDC.

The benefits of data sharing and ensuring open sources of systematic review data.

AIMS: The benefits of increasing public access to data from clinical trials are widely accepted. Such benefits extend to the sharing of data from high-quality systematic reviews, given the time and cost involved with undertaking reviews. We describe the application of open sources of review data, outline potential challenges and highlight efforts made to address these challenges, with the intent of encouraging publishers, funders and authors to consider sharing review data more broadly. RESULTS: We describe the application of systematic review data in: (i) advancing understanding of clinical trials and systematic review methods, (ii) repurposing of data to answer public health policy and practice relevant questions, (iii) identification of research gaps and (iv) accelerating the conduct of rapid reviews to inform decision making. While access, logistical, motivational and legal challenges exist, there has been progress made by systematic review, academic and funding agencies to incentivise data sharing and create infrastructure to support greater access to systematic review data. CONCLUSION: There is opportunity to maximize the benefits of research investment in undertaking systematic reviews by ensuring open sources of systematic review data. Efforts to create such systems should draw on learnings and principles outlined for sharing clinical trial data.

A randomised controlled trial of an implementation strategy delivered at scale to increase outdoor free play opportunities in early childhood education and care (ECEC) services: a study protocol for the get outside get active (GOGA) trial.

BACKGROUND: Increased outdoor play time in young children is associated with many health and developmental benefits. This study aims to evaluate the impact of a multi-strategy implementation strategy delivered at scale, to increase opportunities for outdoor free play in Early Childhood Education and Care (ECEC) services. METHODS: The study will employ a parallel-group randomised controlled trial design. One hundred ECEC services in the Hunter New England region of New South Wales, Australia, will be recruited and randomised to receive either a 6-month implementation strategy or usual care. The trial will seek to increase the implementation of an indoor-outdoor routine (whereby children are allowed to move freely between indoor and outdoor spaces during periods of free play), to increase their opportunity to engage in outdoor free play. Development of the strategy was informed by the Behaviour Change Wheel to address determinants identified in the Theoretical Domains Framework. ECEC services allocated to the control group will receive 'usual' implementation support delivered as part of state-wide obesity prevention programs. The primary trial outcome is the mean minutes/day (calculated across 5 consecutive days) of outdoor free play opportunities provided in ECEC services measured at baseline, 6-months (primary end point) and 18-months post baseline. Analyses will be performed using an intention-to-treat approach with ECEC services as the unit of analysis, using a linear mixed effects regression model to assess between-group differences. A sensitivity analysis will be undertaken, adjusting for service characteristics that appear imbalanced between groups at baseline, and a subgroup analysis examining potential intervention effect among services with the lowest baseline outdoor free play opportunities. DISCUSSION: Identifying effective strategies to support the implementation of indoor-outdoor routines in the ECEC setting at scale is essential to improve child population health. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ( ACTRN12621000987864 ). Prospectively registered 27th July 2021, ANZCTR - Registration.

Feasibility of a Web-Based Implementation Intervention to Improve Child Dietary Intake in Early Childhood Education and Care: Pilot Randomized Controlled Trial.

BACKGROUND: Internationally, the implementation of evidence-based healthy eating policies and practices within early childhood education and care (ECEC) settings that encourage children's healthy diet is recommended. Despite the existence of evidence-based healthy eating practices, research indicates that current implementation rates are inadequate. Web-based approaches provide a potentially effective and less costly approach to support ECEC staff with implementing nutrition policies and practices. OBJECTIVE: The broad aim of this pilot randomized controlled trial is to assess the feasibility of assessing the impact of a web-based program together with health promotion officer (HPO) support on ECEC center implementation of healthy eating policies and practices. Specifically, we seek to describe the completion rate of study evaluation processes (participant consent and data collection rates); examine ECEC center uptake, acceptability, and appropriateness of the intervention and implementation strategies; understand the potential cost of delivering and receiving implementation support strategies; and describe the potential impact of the web-based intervention on the implementation of targeted healthy eating practices among centers in the intervention group. METHODS: A 6-month pilot implementation trial using a cluster-randomized controlled trial design was conducted in 22 ECEC centers within the Hunter New England region of New South Wales, Australia. Potentially eligible centers were distributed a recruitment package and telephoned by the research team to assess eligibility and obtain consent. Centers randomly allocated to the intervention group received access to a web-based program, together with HPO support (eg, educational outreach visit and local technical assistance) to implement 5 healthy eating practices. The web-based program incorporated audit with feedback, development of formal implementation blueprints, and educational materials to facilitate improvement in implementation. The centers allocated to the control group received the usual care. RESULTS: Of the 57 centers approached for the study, 22 (47%) provided consent to participate. Data collection components were completed by 100% (22/22) of the centers. High uptake for implementation strategies provided by HPOs (10/11, 91% to 11/11, 100%) and the web-based program (11/11, 100%) was observed. At follow-up, intervention centers had logged on to the program at an average of 5.18 (SD 2.52) times. The web-based program and implementation support strategies were highly acceptable (10/11, 91% to 11/11, 100%). Implementation of 4 healthy eating practices improved in the intervention group, ranging from 19% (2/11) to 64% (7/11). CONCLUSIONS: This study provides promising pilot data to warrant the conduct of a fully powered implementation trial to assess the impact of the program on ECEC healthy eating practice implementation. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12619001158156; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378099. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s40814-020-00707-w.

Associations Between Digital Health Intervention Engagement, Physical Activity, and Sedentary Behavior: Systematic Review and Meta-analysis.

BACKGROUND: The effectiveness of digital health interventions is commonly assumed to be related to the level of user engagement with the digital health intervention, including measures of both digital health intervention use and users' subjective experience. However, little is known about the relationships between the measures of digital health intervention engagement and physical activity or sedentary behavior. OBJECTIVE: This study aims to describe the direction and strength of the association between engagement with digital health interventions and physical activity or sedentary behavior in adults and explore whether the direction of association of digital health intervention engagement with physical activity or sedentary behavior varies with the type of engagement with the digital health intervention (ie, subjective experience, activities completed, time, and logins). METHODS: Four databases were searched from inception to December 2019. Grey literature and reference lists of key systematic reviews and journals were also searched. Studies were eligible for inclusion if they examined a quantitative association between a measure of engagement with a digital health intervention targeting physical activity and a measure of physical activity or sedentary behavior in adults (aged ≥18 years). Studies that purposely sampled or recruited individuals on the basis of pre-existing health-related conditions were excluded. In addition, studies were excluded if the individual engaging with the digital health intervention was not the target of the physical activity intervention, the study had a non-digital health intervention component, or the digital health interventions targeted multiple health behaviors. A random effects meta-analysis and direction of association vote counting (for studies not included in meta-analysis) were used to address objective 1. Objective 2 used vote counting on the direction of the association. RESULTS: Overall, 10,653 unique citations were identified and 375 full texts were reviewed. Of these, 19 studies (26 associations) were included in the review, with no studies reporting a measure of sedentary behavior. A meta-analysis of 11 studies indicated a small statistically significant positive association between digital health engagement (based on all usage measures) and physical activity (0.08, 95% CI 0.01-0.14, SD 0.11). Heterogeneity was high, with 77% of the variation in the point estimates explained by the between-study heterogeneity. Vote counting indicated that the relationship between physical activity and digital health intervention engagement was consistently positive for three measures: subjective experience measures (2 of 3 associations), activities completed (5 of 8 associations), and logins (6 of 10 associations). However, the direction of associations between physical activity and time-based measures of usage (time spent using the intervention) were mixed (2 of 5 associations supported the hypothesis, 2 were inconclusive, and 1 rejected the hypothesis). CONCLUSIONS: The findings indicate a weak but consistent positive association between engagement with a physical activity digital health intervention and physical activity outcomes. No studies have targeted sedentary behavior outcomes. The findings were consistent across most constructs of engagement; however, the associations were weak.

Correction: Associations Between Digital Health Intervention Engagement, Physical Activity, and Sedentary Behavior: Systematic Review and Meta-analysis.

[This corrects the article DOI: 10.2196/23180.].

Assessing the implementation of healthy eating and physical activity policies and practices in the family day care setting: A cross-sectional study.

ISSUE ADDRESSED: While the family day care setting provides a unique opportunity to improve child health, few studies have assessed obesity prevention practices of this setting. This study aimed to examine the (a) prevalence of implementation of evidence-based healthy eating and physical activity policies and practices among schemes (ie overarching governing agency) and educators in the family day care setting in Australia; and (b) associations between educator socio-demographic characteristics and implementation of healthy eating and physical activity practices. METHODS: Family day care schemes (n = 16) responsible for educators (n = 174) located in the Hunter New England region of NSW participated in a telephone survey in 2018, reporting their implementation of evidence-based healthy eating and physical activity policies and practices. Linear mixed regression analyses were used to determine whether educator characteristics were associated with higher percentages of implementation. RESULTS: Few schemes had comprehensive breastfeeding (0%) and screen time (19%) policies. However, the majority of educators (81%) communicated with families when lunchboxes were not consistent with guidelines. Educators implemented an average of 64.3% of practices assessed. Educators located in higher socioeconomic areas implemented a significantly higher percentage of practices than those in lower socioeconomic areas (P < .000). Educator years of experience was positively associated with percentage of implementation (P = .009). CONCLUSIONS: The implementation of obesity prevention policies and practices in the family day care setting is variable and associated with educator socio-demographic characteristics. SO WHAT?: There is a need to support family day care schemes to improve their obesity prevention environments, particularly those related to policies.

Digital contact tracing technologies in epidemics: a rapid review.

BACKGROUND: Reducing the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global priority. Contact tracing identifies people who were recently in contact with an infected individual, in order to isolate them and reduce further transmission. Digital technology could be implemented to augment and accelerate manual contact tracing. Digital tools for contact tracing may be grouped into three areas: 1) outbreak response; 2) proximity tracing; and 3) symptom tracking. We conducted a rapid review on the effectiveness of digital solutions to contact tracing during infectious disease outbreaks. OBJECTIVES: To assess the benefits, harms, and acceptability of personal digital contact tracing solutions for identifying contacts of an identified positive case of an infectious disease. SEARCH METHODS: An information specialist searched the literature from 1 January 2000 to 5 May 2020 in CENTRAL, MEDLINE, and Embase. Additionally, we screened the Cochrane COVID-19 Study Register. SELECTION CRITERIA: We included randomised controlled trials (RCTs), cluster-RCTs, quasi-RCTs, cohort studies, cross-sectional studies and modelling studies, in general populations. We preferentially included studies of contact tracing during infectious disease outbreaks (including COVID-19, Ebola, tuberculosis, severe acute respiratory syndrome virus, and Middle East respiratory syndrome) as direct evidence, but considered comparative studies of contact tracing outside an outbreak as indirect evidence. The digital solutions varied but typically included software (or firmware) for users to install on their devices or to be uploaded to devices provided by governments or third parties. Control measures included traditional or manual contact tracing, self-reported diaries and surveys, interviews, other standard methods for determining close contacts, and other technologies compared to digital solutions (e.g. electronic medical records). DATA COLLECTION AND ANALYSIS: Two review authors independently screened records and all potentially relevant full-text publications. One review author extracted data for 50% of the included studies, another extracted data for the remaining 50%; the second review author checked all the extracted data. One review author assessed quality of included studies and a second checked the assessments. Our outcomes were identification of secondary cases and close contacts, time to complete contact tracing, acceptability and accessibility issues, privacy and safety concerns, and any other ethical issue identified. Though modelling studies will predict estimates of the effects of different contact tracing solutions on outcomes of interest, cohort studies provide empirically measured estimates of the effects of different contact tracing solutions on outcomes of interest. We used GRADE-CERQual to describe certainty of evidence from qualitative data and GRADE for modelling and cohort studies. MAIN RESULTS: We identified six cohort studies reporting quantitative data and six modelling studies reporting simulations of digital solutions for contact tracing. Two cohort studies also provided qualitative data. Three cohort studies looked at contact tracing during an outbreak, whilst three emulated an outbreak in non-outbreak settings (schools). Of the six modelling studies, four evaluated digital solutions for contact tracing in simulated COVID-19 scenarios, while two simulated close contacts in non-specific outbreak settings. Modelling studies Two modelling studies provided low-certainty evidence of a reduction in secondary cases using digital contact tracing (measured as average number of secondary cases per index case - effective reproductive number (R eff)). One study estimated an 18% reduction in R eff with digital contact tracing compared to self-isolation alone, and a 35% reduction with manual contact-tracing. Another found a reduction in R eff for digital contact tracing compared to self-isolation alone (26% reduction) and a reduction in R eff for manual contact tracing compared to self-isolation alone (53% reduction). However, the certainty of evidence was reduced by unclear specifications of their models, and assumptions about the effectiveness of manual contact tracing (assumed 95% to 100% of contacts traced), and the proportion of the population who would have the app (53%). Cohort studies Two cohort studies provided very low-certainty evidence of a benefit of digital over manual contact tracing. During an Ebola outbreak, contact tracers using an app found twice as many close contacts per case on average than those using paper forms. Similarly, after a pertussis outbreak in a US hospital, researchers found that radio-frequency identification identified 45 close contacts but searches of electronic medical records found 13. The certainty of evidence was reduced by concerns about imprecision, and serious risk of bias due to the inability of contact tracing study designs to identify the true number of close contacts. One cohort study provided very low-certainty evidence that an app could reduce the time to complete a set of close contacts. The certainty of evidence for this outcome was affected by imprecision and serious risk of bias. Contact tracing teams reported that digital data entry and management systems were faster to use than paper systems and possibly less prone to data loss. Two studies from lower- or middle-income countries, reported that contact tracing teams found digital systems simpler to use and generally preferred them over paper systems; they saved personnel time, reportedly improved accuracy with large data sets, and were easier to transport compared with paper forms. However, personnel faced increased costs and internet access problems with digital compared to paper systems. Devices in the cohort studies appeared to have privacy from contacts regarding the exposed or diagnosed users. However, there were risks of privacy breaches from snoopers if linkage attacks occurred, particularly for wearable devices. AUTHORS' CONCLUSIONS: The effectiveness of digital solutions is largely unproven as there are very few published data in real-world outbreak settings. Modelling studies provide low-certainty evidence of a reduction in secondary cases if digital contact tracing is used together with other public health measures such as self-isolation. Cohort studies provide very low-certainty evidence that digital contact tracing may produce more reliable counts of contacts and reduce time to complete contact tracing. Digital solutions may have equity implications for at-risk populations with poor internet access and poor access to digital technology. Stronger primary research on the effectiveness of contact tracing technologies is needed, including research into use of digital solutions in conjunction with manual systems, as digital solutions are unlikely to be used alone in real-world settings. Future studies should consider access to and acceptability of digital solutions, and the resultant impact on equity. Studies should also make acceptability and uptake a primary research question, as privacy concerns can prevent uptake and effectiveness of these technologies.

Strategies to improve the implementation of healthy eating, physical activity and obesity prevention policies, practices or programmes within childcare services.

BACKGROUND: Despite the existence of effective interventions and best-practice guideline recommendations for childcare services to implement evidence-based policies, practices and programmes to promote child healthy eating, physical activity and prevent unhealthy weight gain, many services fail to do so. OBJECTIVES: The primary aim of the review was to examine the effectiveness of strategies aimed at improving the implementation of policies, practices or programmes by childcare services that promote child healthy eating, physical activity and/or obesity prevention. The secondary aims of the review were to: 1. Examine the cost or cost-effectiveness of such strategies; 2. Examine any adverse effects of such strategies on childcare services, service staff or children; 3. Examine the effect of such strategies on child diet, physical activity or weight status. 4. Describe the acceptability, adoption, penetration, sustainability and appropriateness of such implementation strategies. SEARCH METHODS: We searched the following electronic databases on February 22 2019: Cochrane Central Register of Controlled trials (CENTRAL), MEDLINE, MEDLINE In Process, Embase, PsycINFO, ERIC, CINAHL and SCOPUS for relevant studies. We searched reference lists of included studies, handsearched two international implementation science journals, the World Health Organization International Clinical Trials Registry Platform (www.who.int/ictrp/) and ClinicalTrials.gov (www.clinicaltrials.gov). SELECTION CRITERIA: We included any study (randomised or nonrandomised) with a parallel control group that compared any strategy to improve the implementation of a healthy eating, physical activity or obesity prevention policy, practice or programme by staff of centre-based childcare services to no intervention, 'usual' practice or an alternative strategy. Centre-based childcare services included preschools, nurseries, long daycare services and kindergartens catering for children prior to compulsory schooling (typically up to the age of five to six years). DATA COLLECTION AND ANALYSIS: Two review authors independently screened study titles and abstracts, extracted study data and assessed risk of bias; we resolved discrepancies via consensus. We performed meta-analysis using a random-effects model where studies with suitable data and homogeneity were identified; otherwise, findings were described narratively. MAIN RESULTS: Twenty-one studies, including 16 randomised and five nonrandomised, were included in the review. The studies sought to improve the implementation of policies, practices or programmes targeting healthy eating (six studies), physical activity (three studies) or both healthy eating and physical activity (12 studies). Studies were conducted in the United States (n = 12), Australia (n = 8) and Ireland (n = 1). Collectively, the 21 studies included a total of 1945 childcare services examining a range of implementation strategies including educational materials, educational meetings, audit and feedback, opinion leaders, small incentives or grants, educational outreach visits or academic detailing, reminders and tailored interventions. Most studies (n = 19) examined implementation strategies versus usual practice or minimal support control, and two compared alternative implementation strategies. For implementation outcomes, six studies (one RCT) were judged to be at high risk of bias overall. The review findings suggest that implementation strategies probably improve the implementation of policies, practices or programmes that promote child healthy eating, physical activity and/or obesity prevention in childcare services. Of the 19 studies that compared a strategy to usual practice or minimal support control, 11 studies (nine RCTs) used score-based measures of implementation (e.g. childcare service nutrition environment score). Nine of these studies were included in pooled analysis, which found an improvement in implementation outcomes (SMD 0.49; 95% CI 0.19 to 0.79; participants = 495; moderate-certainty evidence). Ten studies (seven RCTs) used dichotomous measures of implementation (e.g. proportion of childcare services implementing a policy or specific practice), with seven of these included in pooled analysis (OR 1.83; 95% CI 0.81 to 4.11; participants = 391; low-certainty evidence). Findings suggest that such interventions probably lead to little or no difference in child physical activity (four RCTs; moderate-certainty evidence) or weight status (three RCTs; moderate-certainty evidence), and may lead to little or no difference in child diet (two RCTs; low-certainty evidence). None of the studies reported the cost or cost-effectiveness of the intervention. Three studies assessed the adverse effects of the intervention on childcare service staff, children and parents, with all studies suggesting they have little to no difference in adverse effects (e.g. child injury) between groups (three RCTs; low-certainty evidence). Inconsistent quality of the evidence was identified across review outcomes and study designs, ranging from very low to moderate. The primary limitation of the review was the lack of conventional terminology in implementation science, which may have resulted in potentially relevant studies failing to be identified based on the search terms used. AUTHORS' CONCLUSIONS: Current research suggests that implementation strategies probably improve the implementation of policies, practices or programmes by childcare services, and may have little or no effect on measures of adverse effects. However such strategies appear to have little to no impact on measures of child diet, physical activity or weight status.

Adaptation, acceptability and feasibility of a Short Food Survey to assess the dietary intake of children during attendance at childcare.

OBJECTIVE: To (i) describe the adaptation of the Short Food Survey (SFS) for assessing the dietary intake of children (2-5 years) during attendance at Early Childhood Education and Care (SFS-ECEC); (ii) determine the acceptability and feasibility of the SFS-ECEC; and (iii) compare the SFS-ECEC to direct observations for assessing dietary intake of children in care. DESIGN: The adapted forty-seven-item SFS-ECEC was completed by childcare educators to capture individual child's usual intake over the past month. Acceptability and feasibility were assessed via educator self-report and completion rates. Mean servings of food groups consumed in accordance with dietary guidelines reported in the SFS-ECEC were compared to those obtained by a single-day direct observation via visual estimation conducted by trained personnel. Mean differences, intra-class correlations, Bland-Altman plots, percentage agreement and Cohen's κ were examined. SETTING: Early Childhood Education and Care, NSW, Australia. PARTICIPANTS: Educators and children. RESULTS: 213 (98·61 %) SFS-ECECs were returned. Acceptability was high with 86·54 % of educators reporting the tool as easy to understand. Mean differences in servings of food groups between the SFS-ECEC and direct observation were statistically significantly different for five out of six foods and ranged 0·08-1·07, with intra-class correlations ranging 0·00-0·21. Agreement between the methods in the classification of children meeting or not meeting dietary guidelines ranged 42·78-93·01 %, with Cohen's κ ranging -0·03 to 0·14. CONCLUSIONS: The SFS-ECEC is acceptable and feasible for completion by childcare educators. While tool refinement and further validation is warranted, small mean differences suggest the tool may be useful in estimating group-level intakes.

A systematic review of interventions to improve the dietary intake, physical activity and weight status of children attending family day care services.

OBJECTIVE: To (i) identify and synthesise findings from interventions to improve the dietary intake, physical activity and weight status of children aged 0-6 years attending family day care services; and (ii) assess the impact of interventions on family day care environments, intervention cost and adverse outcomes. DESIGN: Medline in Process, PsycINFO, ERIC, Embase, CINAHL, CENTRAL and Scopus databases were searched in March 2019. Studies were included if they (i) evaluated an intervention to improve the diet, physical activity and/or weight of children aged 0-6 years; (ii) were delivered in family day care services; (iii) targeted child diet, physical activity and/or weight; and (iv) used a parallel control group design. Screening was undertaken by two reviewers with disagreements resolved by a third reviewer. SETTING: Family day care services, also known as family childcare homes. PARTICIPANTS: Children aged 0-6 years attending family day care services. RESULTS: In total, 8977 titles were retrieved, and 199 full-texts reviewed. No studies met the inclusion criteria for the primary outcome; however, two studies reporting on the secondary outcome of family day care environments were included. The 4-year community-wide obesity prevention programme and the 12-month train-the-trainer programme both reported statistically significant improvements in the healthy eating and physical activity environments of family day care, compared to cross-sectional state-average control groups. CONCLUSIONS: Findings highlight few existing interventions in family day care services and a need for high-quality controlled trials to identify effective interventions to improve children's diet, activity and weight in this setting.

How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks.

BACKGROUND: Optimisation processes have the potential to rapidly improve the impact of health interventions. Optimisation can be defined as a deliberate, iterative and data-driven process to improve a health intervention and/or its implementation to meet stakeholder-defined public health impacts within resource constraints. This study aimed to identify frameworks used to optimise the impact of health interventions and/or their implementation, and characterise the key concepts, steps or processes of identified frameworks. METHODS: A scoping review of MEDLINE, CINAL, PsycINFO, and ProQuest Nursing & Allied Health Source databases was undertaken. Two reviewers independently coded the key concepts, steps or processes involved in each frameworks, and identified if it was a framework aimed to optimise interventions or their implementation. Two review authors then identified the common steps across included frameworks. RESULTS: Twenty optimisation frameworks were identified. Eight frameworks were for optimising interventions, 11 for optimising implementation and one covered both intervention and implementation optimisation. The mean number of steps within the frameworks was six (range 3-9). Almost half (n = 8) could be classified as both linear and cyclic frameworks, indicating that some steps may occur multiple times in a single framework. Two meta-frameworks are proposed, one for intervention optimisation and one for implementation strategy optimisation. Steps for intervention optimisation are: Problem identification; Preparation; Theoretical/Literature base; Pilot/Feasibility testing; Optimisation; Evaluation; and Long-term implementation. Steps for implementation strategy optimisation are: Problem identification; Collaborate; Plan/design; Pilot; Do/change; Study/evaluate/check; Act; Sustain/endure; and Disseminate/extend. CONCLUSIONS: This review provides a useful summary of the common steps followed to optimise a public health intervention or its implementation according to established frameworks. Further opportunities to study and/or validate such frameworks and their impact on improving outcomes exist.