How long does it take to scale-up obesity prevention interventions?

OBJECTIVE: The scale-up of evidence-based interventions is necessary to reverse high rates of obesity. However, scale-up doesn't occur frequently nor in a timely manner. While it has been estimated that takes 14-17 years for research translation to occur, the time taken to scale-up prevention interventions is largely unknown. This study examined the time taken to scale-up obesity prevention interventions across four scale-up pathways. METHODS: A sample of obesity prevention interventions that had been scaled-up or implemented at scale were found using a structured search strategy. Included interventions were mapped against four scale-up pathways and timeframes associated with each stage of the scale-up pathway were identified to determine the time taken to scale-up. RESULTS: Of the 90 interventions found that were scaled-up to at least a city-wide level, less than half reported a comprehensive research pathway to scale-up and a third did not report any evidence of efficacy or effectiveness prior to scale-up. The time taken to scale-up ranged from 0 to 5 years depending on the pathway taken. Those following a comprehensive pathway took approximately 5 years to scale-up, while interventions that had only one evidence generating step took between 1 and 1.5 years to scale-up. For the remaining interventions, scale-up occurred immediately post-development without evidence generation. CONCLUSIONS: Our findings indicate that the scale-up of obesity prevention interventions can occur more quickly than previous estimates of 14-17 years. Our findings support previous research that scale-up of interventions occurs through a variety of pathways and often scale-up occurs in absence of prior evidence of effectiveness.

Factors associated with child and adolescent electronic nicotine and non-nicotine delivery systems use: A scoping review.

OBJECTIVES: To identify, characterise and broadly synthesise factors associated with child and adolescent electronic nicotine delivery systems (ENDS) and/or electronic non-nicotine delivery systems (ENNDS) ever-use and/or current use. METHODS: Four electronic databases were searched from inception to 3rd June 2022. Non-experimental studies that provided quantitative factors associated with adolescent and/or child ENDS or ENNDS ever-use and/or current use were included. Factors associated with ever-use (any lifetime use) and/or current use (use in past 30 days) were included. All screening and data extraction was conducted independently by paired review authors. Frequencies for country, study design, sample size, measure of ENDS/ENNDS use and factors examined were calculated. Factors were categorised according to the Theory of Triadic Influence domains and sub-domains. RESULTS: The search of electronic databases identified 4756 records, 240 of which were included. The majority of studies examined factors categorised within the Biology and Personality domain of the Theory of Triadic Influence (89.2%; 95%CI 84.6, 82.5), followed by the Social Context (50.8%; 95%CI 44.5, 57.2) and Broader Environment domains (30.4%; 95%CI 24.6, 36.3). The proportion of factors significantly associated with ENDS/ENNDS use was >75% for the Behavioural (78.0%; factors included use of tobacco, other drugs and alcohol), Peer Attitudes and Behaviours (80.0%; factors included peer use of ENDS/ENNDS and tobacco), and Legislation/Policy sub-domains (78.6%; factors included accessibility and advertising). CONCLUSIONS: The evidence base on factors associated with ENDS/ENNDS use in children and adolescents is rapidly developing, predominately by research concentrated in high income regions and focused on behavioural- and personality-related factors.

Interventions to prevent obesity in children aged 5 to 11 years old.

BACKGROUND: Prevention of obesity in children is an international public health priority given the prevalence of the condition (and its significant impact on health, development and well-being). Interventions that aim to prevent obesity involve behavioural change strategies that promote healthy eating or 'activity' levels (physical activity, sedentary behaviour and/or sleep) or both, and work by reducing energy intake and/or increasing energy expenditure, respectively. There is uncertainty over which approaches are more effective and numerous new studies have been published over the last five years, since the previous version of this Cochrane review. OBJECTIVES: To assess the effects of interventions that aim to prevent obesity in children by modifying dietary intake or 'activity' levels, or a combination of both, on changes in BMI, zBMI score and serious adverse events. SEARCH METHODS: We used standard, extensive Cochrane search methods. The latest search date was February 2023. SELECTION CRITERIA: Randomised controlled trials in children (mean age 5 years and above but less than 12 years), comparing diet or 'activity' interventions (or both) to prevent obesity with no intervention, usual care, or with another eligible intervention, in any setting. Studies had to measure outcomes at a minimum of 12 weeks post baseline. We excluded interventions designed primarily to improve sporting performance. DATA COLLECTION AND ANALYSIS: We used standard Cochrane methods. Our outcomes were body mass index (BMI), zBMI score and serious adverse events, assessed at short- (12 weeks to < 9 months from baseline), medium- (9 months to < 15 months) and long-term (≥ 15 months) follow-up. We used GRADE to assess the certainty of the evidence for each outcome. MAIN RESULTS: This review includes 172 studies (189,707 participants); 149 studies (160,267 participants) were included in meta-analyses. One hundred forty-six studies were based in high-income countries. The main setting for intervention delivery was schools (111 studies), followed by the community (15 studies), the home (eight studies) and a clinical setting (seven studies); one intervention was conducted by telehealth and 31 studies were conducted in more than one setting. Eighty-six interventions were implemented for less than nine months; the shortest was conducted over one visit and the longest over four years. Non-industry funding was declared by 132 studies; 24 studies were funded in part or wholly by industry. Dietary interventions versus control Dietary interventions, compared with control, may have little to no effect on BMI at short-term follow-up (mean difference (MD) 0, 95% confidence interval (CI) -0.10 to 0.10; 5 studies, 2107 participants; low-certainty evidence) and at medium-term follow-up (MD -0.01, 95% CI -0.15 to 0.12; 9 studies, 6815 participants; low-certainty evidence) or zBMI at long-term follow-up (MD -0.05, 95% CI -0.10 to 0.01; 7 studies, 5285 participants; low-certainty evidence). Dietary interventions, compared with control, probably have little to no effect on BMI at long-term follow-up (MD -0.17, 95% CI -0.48 to 0.13; 2 studies, 945 participants; moderate-certainty evidence) and zBMI at short- or medium-term follow-up (MD -0.06, 95% CI -0.13 to 0.01; 8 studies, 3695 participants; MD -0.04, 95% CI -0.10 to 0.02; 9 studies, 7048 participants; moderate-certainty evidence). Five studies (1913 participants; very low-certainty evidence) reported data on serious adverse events: one reported serious adverse events (e.g. allergy, behavioural problems and abdominal discomfort) that may have occurred as a result of the intervention; four reported no effect. Activity interventions versus control Activity interventions, compared with control, may have little to no effect on BMI and zBMI at short-term or long-term follow-up (BMI short-term: MD -0.02, 95% CI -0.17 to 0.13; 14 studies, 4069 participants; zBMI short-term: MD -0.02, 95% CI -0.07 to 0.02; 6 studies, 3580 participants; low-certainty evidence; BMI long-term: MD -0.07, 95% CI -0.24 to 0.10; 8 studies, 8302 participants; zBMI long-term: MD -0.02, 95% CI -0.09 to 0.04; 6 studies, 6940 participants; low-certainty evidence). Activity interventions likely result in a slight reduction of BMI and zBMI at medium-term follow-up (BMI: MD -0.11, 95% CI -0.18 to -0.05; 16 studies, 21,286 participants; zBMI: MD -0.05, 95% CI -0.09 to -0.02; 13 studies, 20,600 participants; moderate-certainty evidence). Eleven studies (21,278 participants; low-certainty evidence) reported data on serious adverse events; one study reported two minor ankle sprains and one study reported the incident rate of adverse events (e.g. musculoskeletal injuries) that may have occurred as a result of the intervention; nine studies reported no effect. Dietary and activity interventions versus control Dietary and activity interventions, compared with control, may result in a slight reduction in BMI and zBMI at short-term follow-up (BMI: MD -0.11, 95% CI -0.21 to -0.01; 27 studies, 16,066 participants; zBMI: MD -0.03, 95% CI -0.06 to 0.00; 26 studies, 12,784 participants; low-certainty evidence) and likely result in a reduction of BMI and zBMI at medium-term follow-up (BMI: MD -0.11, 95% CI -0.21 to 0.00; 21 studies, 17,547 participants; zBMI: MD -0.05, 95% CI -0.07 to -0.02; 24 studies, 20,998 participants; moderate-certainty evidence). Dietary and activity interventions compared with control may result in little to no difference in BMI and zBMI at long-term follow-up (BMI: MD 0.03, 95% CI -0.11 to 0.16; 16 studies, 22,098 participants; zBMI: MD -0.02, 95% CI -0.06 to 0.01; 22 studies, 23,594 participants; low-certainty evidence). Nineteen studies (27,882 participants; low-certainty evidence) reported data on serious adverse events: four studies reported occurrence of serious adverse events (e.g. injuries, low levels of extreme dieting behaviour); 15 studies reported no effect. Heterogeneity was apparent in the results for all outcomes at the three follow-up times, which could not be explained by the main setting of the interventions (school, home, school and home, other), country income status (high-income versus non-high-income), participants' socioeconomic status (low versus mixed) and duration of the intervention. Most studies excluded children with a mental or physical disability. AUTHORS' CONCLUSIONS: The body of evidence in this review demonstrates that a range of school-based 'activity' interventions, alone or in combination with dietary interventions, may have a modest beneficial effect on obesity in childhood at short- and medium-term, but not at long-term follow-up. Dietary interventions alone may result in little to no difference. Limited evidence of low quality was identified on the effect of dietary and/or activity interventions on severe adverse events and health inequalities; exploratory analyses of these data suggest no meaningful impact. We identified a dearth of evidence for home and community-based settings (e.g. delivered through local youth groups), for children living with disabilities and indicators of health inequities.

Interventions to prevent obesity in children aged 12 to 18 years old.

BACKGROUND: Prevention of obesity in adolescents is an international public health priority. The prevalence of overweight and obesity is over 25% in North and South America, Australia, most of Europe, and the Gulf region. Interventions that aim to prevent obesity involve strategies that promote healthy diets or 'activity' levels (physical activity, sedentary behaviour and/or sleep) or both, and work by reducing energy intake and/or increasing energy expenditure, respectively. There is uncertainty over which approaches are more effective, and numerous new studies have been published over the last five years since the previous version of this Cochrane Review. OBJECTIVES: To assess the effects of interventions that aim to prevent obesity in adolescents by modifying dietary intake or 'activity' levels, or a combination of both, on changes in BMI, zBMI score and serious adverse events. SEARCH METHODS: We used standard, extensive Cochrane search methods. The latest search date was February 2023. SELECTION CRITERIA: Randomised controlled trials in adolescents (mean age 12 years and above but less than 19 years), comparing diet or 'activity' interventions (or both) to prevent obesity with no intervention, usual care, or with another eligible intervention, in any setting. Studies had to measure outcomes at a minimum of 12 weeks post baseline. We excluded interventions designed primarily to improve sporting performance. DATA COLLECTION AND ANALYSIS: We used standard Cochrane methods. Our outcomes were BMI, zBMI score and serious adverse events, assessed at short- (12 weeks to < 9 months from baseline), medium- (9 months to < 15 months) and long-term (≥ 15 months) follow-up. We used GRADE to assess the certainty of the evidence for each outcome. MAIN RESULTS: This review includes 74 studies (83,407 participants); 54 studies (46,358 participants) were included in meta-analyses. Sixty studies were based in high-income countries. The main setting for intervention delivery was schools (57 studies), followed by home (nine studies), the community (five studies) and a primary care setting (three studies). Fifty-one interventions were implemented for less than nine months; the shortest was conducted over one visit and the longest over 28 months. Sixty-two studies declared non-industry funding; five were funded in part by industry. Dietary interventions versus control The evidence is very uncertain about the effects of dietary interventions on body mass index (BMI) at short-term follow-up (mean difference (MD) -0.18, 95% confidence interval (CI) -0.41 to 0.06; 3 studies, 605 participants), medium-term follow-up (MD -0.65, 95% CI -1.18 to -0.11; 3 studies, 900 participants), and standardised BMI (zBMI) at long-term follow-up (MD -0.14, 95% CI -0.38 to 0.10; 2 studies, 1089 participants); all very low-certainty evidence. Compared with control, dietary interventions may have little to no effect on BMI at long-term follow-up (MD -0.30, 95% CI -1.67 to 1.07; 1 study, 44 participants); zBMI at short-term (MD -0.06, 95% CI -0.12 to 0.01; 5 studies, 3154 participants); and zBMI at medium-term (MD 0.02, 95% CI -0.17 to 0.21; 1 study, 112 participants) follow-up; all low-certainty evidence. Dietary interventions may have little to no effect on serious adverse events (two studies, 377 participants; low-certainty evidence). Activity interventions versus control Compared with control, activity interventions do not reduce BMI at short-term follow-up (MD -0.64, 95% CI -1.86 to 0.58; 6 studies, 1780 participants; low-certainty evidence) and probably do not reduce zBMI at medium- (MD 0, 95% CI -0.04 to 0.05; 6 studies, 5335 participants) or long-term (MD -0.05, 95% CI -0.12 to 0.02; 1 study, 985 participants) follow-up; both moderate-certainty evidence. Activity interventions do not reduce zBMI at short-term follow-up (MD 0.02, 95% CI -0.01 to 0.05; 7 studies, 4718 participants; high-certainty evidence), but may reduce BMI slightly at medium-term (MD -0.32, 95% CI -0.53 to -0.11; 3 studies, 2143 participants) and long-term (MD -0.28, 95% CI -0.51 to -0.05; 1 study, 985 participants) follow-up; both low-certainty evidence. Seven studies (5428 participants; low-certainty evidence) reported data on serious adverse events: two reported injuries relating to the exercise component of the intervention and five reported no effect of intervention on reported serious adverse events. Dietary and activity interventions versus control Dietary and activity interventions, compared with control, do not reduce BMI at short-term follow-up (MD 0.03, 95% CI -0.07 to 0.13; 11 studies, 3429 participants; high-certainty evidence), and probably do not reduce BMI at medium-term (MD 0.01, 95% CI -0.09 to 0.11; 8 studies, 5612 participants; moderate-certainty evidence) or long-term (MD 0.06, 95% CI -0.04 to 0.16; 6 studies, 8736 participants; moderate-certainty evidence) follow-up. They may have little to no effect on zBMI in the short term, but the evidence is very uncertain (MD -0.09, 95% CI -0.2 to 0.02; 3 studies, 515 participants; very low-certainty evidence), and they may not reduce zBMI at medium-term (MD -0.05, 95% CI -0.1 to 0.01; 6 studies, 3511 participants; low-certainty evidence) or long-term (MD -0.02, 95% CI -0.05 to 0.01; 7 studies, 8430 participants; low-certainty evidence) follow-up. Four studies (2394 participants) reported data on serious adverse events (very low-certainty evidence): one reported an increase in weight concern in a few adolescents and three reported no effect. AUTHORS' CONCLUSIONS: The evidence demonstrates that dietary interventions may have little to no effect on obesity in adolescents. There is low-certainty evidence that activity interventions may have a small beneficial effect on BMI at medium- and long-term follow-up. Diet plus activity interventions may result in little to no difference. Importantly, this updated review also suggests that interventions to prevent obesity in this age group may result in little to no difference in serious adverse effects. Limitations of the evidence include inconsistent results across studies, lack of methodological rigour in some studies and small sample sizes. Further research is justified to investigate the effects of diet and activity interventions to prevent childhood obesity in community settings, and in young people with disabilities, since very few ongoing studies are likely to address these. Further randomised trials to address the remaining uncertainty about the effects of diet, activity interventions, or both, to prevent childhood obesity in schools (ideally with zBMI as the measured outcome) would need to have larger samples.

Long-Term Effectiveness of a Multi-Strategy Choice Architecture Intervention in Increasing Healthy Food Choices of High-School Students From Online Canteens (Click & Crunch High Schools): Cluster Randomized Controlled Trial.

BACKGROUND: School canteens are a recommended setting to influence adolescent nutrition due to their scope to improve student food choices. Online lunch ordering systems ("online canteens") are increasingly used and represent attractive infrastructure to implement choice architecture interventions that nudge users toward healthier food choices. A recent cluster randomized controlled trial demonstrated the short-term effectiveness (2-month follow-up) of a choice architecture intervention to increase the healthiness of foods purchased by high school students from online canteens. However, there is little evidence regarding the long-term effectiveness of choice architecture interventions targeting adolescent food purchases, particularly those delivered online. OBJECTIVE: This study aimed to determine the long-term effectiveness of a multi-strategy choice architecture intervention embedded within online canteen infrastructure in high schools at a 15-month follow-up. METHODS: A cluster randomized controlled trial was undertaken with 1331 students (from 9 high schools) in New South Wales, Australia. Schools were randomized to receive the automated choice architecture intervention (including menu labeling, positioning, feedback, and prompting strategies) or the control (standard online ordering). The foods purchased were classified according to the New South Wales Healthy Canteen strategy as either "everyday," "occasional," or "should not be sold." Primary outcomes were the average proportion of "everyday," "occasional," and "should not be sold" items purchased per student. Secondary outcomes were the mean energy, saturated fat, sugar, and sodium content of purchases. Outcomes were assessed using routine data collected by the online canteen. RESULTS: From baseline to 15-month follow-up, on average, students in the intervention group ordered significantly more "everyday" items (+11.5%, 95% CI 7.3% to 15.6%; P<.001), and significantly fewer "occasional" (-5.4%, 95% CI -9.4% to -1.5%; P=.007) and "should not be sold" items (-6%, 95% CI -9.1% to -2.9%; P<.001), relative to controls. There were no between-group differences over time in the mean energy, saturated fat, sugar, or sodium content of lunch orders. CONCLUSIONS: Given their longer-term effectiveness, choice architecture interventions delivered via online canteens may represent a promising option for policy makers to support healthy eating among high school students. TRIAL REGISTRATION: Australian Clinical Trials ACTRN12620001338954, https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380546 ; Open Science Framework osf.io/h8zfr, https://osf.io/h8zfr/.

Research outcomes informing the selection of public health interventions and strategies to implement them: A cross-sectional survey of Australian policy-maker and practitioner preferences.

BACKGROUND: A key role of public health policy-makers and practitioners is to ensure beneficial interventions are implemented effectively enough to yield improvements in public health. The use of evidence to guide public health decision-making to achieve this is recommended. However, few studies have examined the relative value, as reported by policy-makers and practitioners, of different broad research outcomes (that is, measures of cost, acceptability, and effectiveness). To guide the conduct of research and better inform public health policy and practice, this study aimed at describing the research outcomes that Australian policy-makers and practitioners consider important for their decision-making when selecting: (a) public health interventions; (b) strategies to support their implementation; and (c) to assess the differences in research outcome preferences between policy-makers and practitioners. METHOD: An online value-weighting survey was conducted with Australian public health policy-makers and practitioners working in the field of non-communicable disease prevention. Participants were presented with a list of research outcomes and were asked to select up to five they considered most critical to their decision-making. They then allocated 100 points across these - allocating more points to outcomes perceived as more important. Outcome lists were derived from a review and consolidation of evaluation and outcome frameworks in the fields of public health knowledge translation and implementation. We used descriptive statistics to report relative preferences overall and for policy-makers and practitioners separately. RESULTS: Of the 186 participants; 90 primarily identified as policy-makers and 96 as public health prevention practitioners. Overall, research outcomes of effectiveness, equity, feasibility, and sustainability were identified as the four most important outcomes when considering either interventions or strategies to implement them. Scores were similar for most outcomes between policy-makers and practitioners. CONCLUSION: For Australian policy-makers and practitioners working in the field of non-communicable disease prevention, outcomes related to effectiveness, equity, feasibility, and sustainability appear particularly important to their decisions about the interventions they select and the strategies they employ to implement them. The findings suggest researchers should seek to meet these information needs and prioritize the inclusion of such outcomes in their research and dissemination activities. The extent to which these outcomes are critical to informing the decision of policy-makers and practitioners working in other jurisdictions or contexts warrants further investigation.

Unpacking the cost of the lunchbox for Australian families: a secondary analysis.

Ninety per cent of Australian school children bring a home-packed lunch to school, with 44% of the food consumed during school hours being unhealthy. Among other factors, cost is a key consideration for food provision; however, the costs to Australian families are not well understood. Therefore, we aimed to determine what families are currently paying for school lunchboxes in Australian primary schools and to examine associations between food costs and socio-demographic factors with dietary quality. An audit of local retail outlets was used to determine the food costs of lunchbox contents. Costs (AUD) were adjusted for inflation as of early 2023. The lunchboxes of 1026 children aged 4-12 years at 12 Catholic primary schools in New South Wales, Australia, were assessed at the start of the day, using photography assessment methods and a validated School Food Checklist. The mean cost of lunchbox contents was $4.48 AUD (SD 1.53), containing a mean energy of 2699 kJ (SD 859), with 37.3% (SD 23.9) of energy sourced from unhealthy foods. Multiple linear regression analyses found that the strongest predictors of higher lunchbox cost (P < 0.05) were a higher proportion of energy from unhealthy foods (B = 0.016) and lower Socio-Economic Indexes for Areas (B = -0.178), when controlling for child socio-demographics. The results indicated that lunchbox food costs to Australian families are comparable to alternative school food service models in Australia and internationally. Results demonstrate the cost of food is not the only barrier to providing a healthy school lunchbox. Demonstrating a need for cost-considerate systematic interventions addressing food provision challenges and socio-economic disparities faced by families.

Evidence on Scaling in Health and Social Care: An Umbrella Review.

Policy Points More rigorous methodologies and systematic approaches should be encouraged in the science of scaling. This will help researchers better determine the effectiveness of scaling, guide stakeholders in the scaling process, and ultimately increase the impacts of health innovations. The practice and the science of scaling need to expand worldwide to address complex health conditions such as noncommunicable and chronic diseases. Although most of the scaling experiences described in the literature are occurring in the Global South, most of the authors publishing on it are based in the Global North. As the science of scaling spreads across the world with the aim of reducing health inequities, it is also essential to address the power imbalance in how we do scaling research globally. CONTEXT: Scaling of effective innovations in health and social care is essential to increase their impact. We aimed to synthesize the evidence base on scaling and identify current knowledge gaps. METHODS: We conducted an umbrella review according to the Joanna Briggs Institute Reviewers' Manual. We included any type of review that 1) focused on scaling, 2) covered health or social care, and 3) presented a methods section. We searched MEDLINE (Ovid), Embase, PsycINFO (Ovid), CINAHL (EBSCO), Web of Science, The Cochrane Library, Sociological Abstracts (ProQuest), Academic Search Premier (EBSCO), and ProQuest Dissertations & Theses Global from their inception to August 6, 2020. We searched the gray literature using, e.g., Google and WHO-ExpandNet. We assessed methodological quality with AMSTAR2. Paired reviewers independently selected and extracted eligible reviews and assessed study quality. A narrative synthesis was performed. FINDINGS: Of 24,269 records, 137 unique reviews were included. The quality of the 58 systematic reviews was critically low (n = 42). The most frequent review type was systematic review (n = 58). Most reported on scaling in low- and middle-income countries (n = 59), whereas most first authors were from high-income countries (n = 114). Most reviews concerned infectious diseases (n = 36) or maternal-child health (n = 28). They mainly focused on interventions (n = 37), barriers and facilitators (n = 29), frameworks (n = 24), scalability (n = 24), and costs (n = 14). The WHO/ExpandNet scaling definition was the definition most frequently used (n = 26). Domains most reported as influencing scaling success were building scaling infrastructure (e.g., creating new service sites) and human resources (e.g., training community health care providers). CONCLUSIONS: The evidence base on scaling is evolving rapidly as reflected by publication trends, the range of focus areas, and diversity of scaling definitions. Our study highlights knowledge gaps around methodology and research infrastructures to facilitate equitable North-South research relationships. Common efforts are needed to ensure scaling expands the impacts of health and social innovations to broader populations.

Evaluating the scaling up of an effective implementation intervention (PACE) to increase the delivery of a mandatory physical activity policy in primary schools.

BACKGROUND: Physically Active Children in Education (PACE) is an effective implementation intervention for increasing the number of minutes classroom teachers schedule physical activity each week. To date, evaluations of PACE have included a smaller number of schools from only one region in New South Wales Australia. If PACE is to have population-wide benefits we must be able to deliver this support to a larger number of schools across multiple regions. This study aimed to evaluate the scale-up of PACE. METHODS: An uncontrolled before and after study, with 100 schools from three regions was conducted. Participating schools received PACE for approximately 12 months. We assessed the following outcomes: delivery of the evidence-based intervention (EBI) (i.e. minutes of physical activity scheduled by classroom teachers per week); delivery of the implementation strategies (i.e. reach, dose delivered, adherence and indicators of sustainability); and key determinants of implementation (i.e. acceptability of strategies and cost). Data were collected via project officer records, and principal and teacher surveys. Linear mixed models were used to assess EBI delivery by evaluating the difference in the mean minutes teachers scheduled physical activity per week from baseline to follow-up. Descriptive data were used to assess delivery of the implementation strategies and their perceived acceptability (i.e. PACE). A prospective, trial-based economic evaluation was used to assess cost. RESULTS: Delivery of the EBI was successful: teachers increas their average minutes of total physical activity scheduled across the school week by 26.8 min (95% CI: 21.2, 32.4, p < 0.001) after receiving PACE. Indicators for delivery of implementation strategies were high: 90% of consenting schools received all strategies and components (reach); 100% of strategies were delivered by the provider (dose); >50% of schools adhered to the majority of strategies (11 of the 14 components); and acceptability was > 50% agreement for all strategies. The incremental cost per additional minute of physical activity scheduled per week was $27 per school (Uncertainty Interval $24, $31). CONCLUSIONS: PACE can be successfully delivered across multiple regions and to a large number of schools. Given the ongoing and scalable benefits of PACE, it is important that we continue to extend and improve this program while considering ways to reduce the associated cost.

Interventions to prevent or cease electronic cigarette use in children and adolescents.

BACKGROUND: The prevalence of e-cigarette use has increased globally amongst children and adolescents in recent years. In response to the increasing prevalence and emerging evidence about the potential harms of e-cigarettes in children and adolescents, leading public health organisations have called for approaches to address increasing e-cigarette use. Whilst evaluations of approaches to reduce uptake and use regularly appear in the literature, the collective long-term benefit of these is currently unclear. OBJECTIVES: The co-primary objectives of the review were to: (1) evaluate the effectiveness of interventions to prevent e-cigarette use in children and adolescents (aged 19 years and younger) with no prior use, relative to no intervention, waitlist control, usual practice, or an alternative intervention; and (2) evaluate the effectiveness of interventions to cease e-cigarette use in children and adolescents (aged 19 years and younger) reporting current use, relative to no intervention, waitlist control, usual practice, or an alternative intervention. Secondary objectives were to: (1) examine the effect of such interventions on child and adolescent use of other tobacco products (e.g. cigarettes, cigars types, and chewing tobacco); and (2) describe the unintended adverse effects of the intervention on individuals (e.g. physical or mental health of individuals), or on organisations (e.g. intervention displacement of key curricula or learning opportunities for school students) where such interventions are being implemented. SEARCH METHODS: We searched CENTRAL, Ovid MEDLINE, Ovid Embase, Ovid PsycINFO, EBSCO CINAHL, and Clarivate Web of Science Core Collection from inception to 1 May 2023. Additionally, we searched two trial registry platforms (WHO International Clinical Trials Registry Platform; US National Institutes of Health Ongoing Trials Register ClinicalTrials.gov), Google Scholar, and the reference lists of relevant systematic reviews. We contacted corresponding authors of articles identified as ongoing studies. SELECTION CRITERIA: We included randomised controlled trials (RCTs), including cluster-RCTs, factorial RCTs, and stepped-wedge RCTs. To be eligible, the primary targets of the interventions must have been children and adolescents aged 19 years or younger. Interventions could have been conducted in any setting, including community, school, health services, or the home, and must have sought to influence children or adolescent (or both) e-cigarette use directly. Studies with a comparator of no intervention (i.e. control), waitlist control, usual practice, or an alternative intervention not targeting e-cigarette use were eligible. We included measures to assess the effectiveness of interventions to: prevent child and adolescent e-cigarette use (including measures of e-cigarette use amongst those who were never-users); and cease e-cigarette use (including measures of e-cigarette use amongst children and adolescents who were e-cigarette current-users). Measures of e-cigarette use included current-use (defined as use in the past 30 days) and ever-use (defined as any lifetime use). DATA COLLECTION AND ANALYSIS: Two review authors independently screened the titles and abstracts of references, with any discrepancies resolved through consensus. Pairs of review authors independently assessed the full-text articles for inclusion in the review. We planned for two review authors to independently extract information from the included studies and assess risk of bias using the Cochrane RoB 2 tool. We planned to conduct multiple meta-analyses using a random-effects model to align with the co-primary objectives of the review. First, we planned to pool interventions to prevent child and adolescent e-cigarette use and conduct two analyses using the outcome measures of 'ever-use' and 'current-use'. Second, we planned to pool interventions to cease child and adolescent e-cigarette use and conduct one analysis using the outcome measure of 'current-use'. Where data were unsuitable for pooling in meta-analyses, we planned to conduct a narrative synthesis using vote-counting approaches and to follow the Cochrane Handbook for Systematic Reviews of Interventions and the Synthesis Without Meta-analysis (SWiM) guidelines. MAIN RESULTS: The search of electronic databases identified 7141 citations, with a further 287 records identified from the search of trial registries and Google Scholar. Of the 110 studies (116 records) evaluated in full text, we considered 88 to be ineligible for inclusion for the following reasons: inappropriate outcome (27 studies); intervention (12 studies); study design (31 studies); and participants (18 studies). The remaining 22 studies (28 records) were identified as ongoing studies that may be eligible for inclusion in a future review update. We identified no studies with published data that were eligible for inclusion in the review. AUTHORS' CONCLUSIONS: We identified no RCTs that met the inclusion criteria for the review, and as such, there is no evidence available from RCTs to assess the potential impact of interventions targeting children and adolescent e-cigarette use, tobacco use, or any unintended adverse effects. Evidence from studies employing other trial designs (e.g. non-randomised) may exist; however, such studies were not eligible for inclusion in the review. Evidence from studies using non-randomised designs should be examined to guide actions to prevent or cease e-cigarette use. This is a living systematic review. We search for new evidence every month and update the review when we identify relevant new evidence. Please refer to the Cochrane Database of Systematic Reviews for the current status of this review.

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.

An audit of the dissemination strategies and plan included in international food-based dietary guidelines.

OBJECTIVE: Food-based dietary guidelines (FBDG) are an important resource to improve population health; however, little is known about the types of strategies to disseminate them. This study sought to describe dissemination strategies and content of dissemination plans that were available for FBDG. DESIGN: A cross-sectional audit of FBDG with a published English-language version sourced from the United Nations FAO repository. We searched for publicly available dissemination strategies and any corresponding plans available in English language. Two authors extracted data on strategies, which were grouped according to the Model for Dissemination Research Framework (including source, audience, channel and message). For guidelines with a dissemination plan, we described goals, audience, strategies and expertise and resources according to the Canadian Institute for Health Research guidance. SETTING: FBDG from fifty-three countries mostly from high-income (n 28, 52·8 %), and upper-middle income (n 18, 34 %) areas were included. PARTICIPANTS: n/a. RESULTS: The source of guidelines was most frequently health departments (79·2 %). The message included quantities and types of foods, physical activity recommendations and 88·7 % included summarised versions of main messages. The most common channels were infographics and information booklets, and the main end-users were the public. For twelve countries (22·6 %), we were able to source an English-language dissemination plan, where none met all recommendations outlined by the Canadian Institute for Health Research. CONCLUSIONS: The public was the most frequently identified end-user and thus most dissemination strategies and plans focused on this group. Few FBDG had formal dissemination plans and of those there was limited detailed provided.

Improving academic and public health impact of Cochrane public health reviews: what can we learn from bibliographic metrics and author dissemination strategies? A cross-sectional study.

BACKGROUND: To facilitate the development of impactful research dissemination strategies, this study aimed to: (i) survey authors of trials included in a sample of Cochrane reviews to describe strategies to disseminate trial findings, and examine their association with academic and policy impacts and (ii) audit academic and policy impact of CPH reviews. METHODS: Authors of 104 trials within identified Cochrane reviews completed survey items assessing the dissemination strategies. Field weighted citation (FWCI) data extracted from bibliographic databases served as a measure of academic impact of trials and CPH reviews. Policy and practice impacts of trials were assessed during the survey of trial authors using items based on the Payback Framework, and for CPH reviews using 'policy mention' data collected via Altmetric Explorer. RESULTS: Among the included trials, univariate (but not multivariable) regression models revealed significant associations between the use of dissemination strategies (i.e. posts on social media; workshops with end-users; media-releases) and policy or practice impacts. No significant associations were reported between dissemination strategies and trial FWCI. The mean FWCI of CPH reviews suggest that they are cited 220% more than other reviews in their field. CONCLUSIONS: Comprehensive dissemination strategies are likely required to maximize the potential the potential impacts of public health research.

The impact of an e-newsletter or animated video to disseminate outdoor free-play information in relation to COVID-19 guidelines in New South Wales early childhood education and care services: a randomised controlled trial.

BACKGROUND: State-based Guidelines were issued for Early Childhood Education and Care (ECEC) services (caring for children 0-6 years) recommending greater time outdoors and inclusion of indoor-outdoor programs to facilitate social distancing to reduce spread of COVID-19. The aim of this 3-arm randomised controlled trial (RCT) was to examine the impact of different dissemination strategies on increasing ECEC service intentions to adopt recommendations from the Guidelines. METHODS: This was a post-intervention only RCT. A sample of eligible ECEC services in New South Wales (n = 1026) were randomly allocated to one of three groups; (i) e-newsletter resource; (ii) animated video resource; or (iii) control (standard email). The intervention was designed to address key determinants of guideline adoption including awareness and knowledge. Following delivery of the intervention in September 2021, services were invited to participate in an online or telephone survey from October-December 2021. The primary trial outcome was the proportion of services intending to adopt the Guidelines, defined as intention to; (i) offer an indoor-outdoor program for the full day; or (ii) offer more outdoor play time. Secondary outcomes included awareness, reach, knowledge and implementation of the Guidelines. Barriers to Guideline implementation, cost of the dissemination strategies and analytic data to measure fidelity of intervention delivery were also captured. RESULTS: Of the 154 services that provided post-intervention data, 58 received the e-newsletter (37.7%), 50 received the animated video (32.5%), and 46 received the control (29.9%). Services who received the animated video had nearly five times the odds (OR: 4.91 [1.03, 23.34] p = 0.046) than those in the control group, to report having intentions to adopt the Guidelines. There were no statistically significant differences in awareness or knowledge of the Guidelines between either intervention or control services. Development costs were greatest for the animated video. The extent to which the dissemination strategy was viewed in full, were similar for both the e-newsletter and animated video. CONCLUSION: This study found potential for the inclusion of interactive strategies to disseminate policy and guideline information within the ECEC setting, in the context of the need for rapid communication. Further research should explore the added benefits of embedding such strategies within a multi-strategy intervention. TRIAL REGISTRATION: Retrospectively registered with the Australian New Zealand Clinical Trials Registry (ANZCTR) on the 23/02/2023 (ACTRN 12,623,000,198,628).

Factors associated with physical activity policy and practice implementation in British Columbia's childcare settings: a longitudinal study.

BACKGROUND: In 2016-17, the government of British Columbia (BC) enacted a mandatory policy outlining Active Play Standards (AP Standards) alongside a capacity building initiative (Appetite to Play) focused on implementing policies and practices to support physical activity in childcare centres. We aimed to identify factors at the provider and organizational levels as well as attributes of the Standards hypothesized to influence implementation (i.e., changes in policies and practices). METHODS: We conducted surveys before (2016-2017) and after (2018-2019) enforcement of the AP Standards among 146 group childcare centres across BC. The 2018-19 surveys measured theoretically based constructs associated with implementation of policies and practices (9 childcare- and 8 provider- level characteristics as well as 4 attributes of the licensing standards). Characteristics that were associated in simple regression models were entered in multivariable regression models to identify factors associated with policy and practice changes related to fundamental movement skills (FMS), screen time, total amount of active play (AP) and total amount of outdoor AP from baseline to follow-up. RESULTS: In multivariable analyses, higher staff capacity (OR = 2.1, 95% 1.2, 3.7) and perceived flexibility of the standards (OR: 3.3, 95% 1.5, 7.1) were associated with higher odds of a policy change related to FMS. Higher staff commitment to the AP standards was associated with a higher odds of policy changes related to screen time (OR = 1.6, 95% CI: 1.1, 2.4) and amount of AP (OR: 1.5, 95% 1.0, 2.3). Higher institutionalization of PA policies was associated with a higher odds of policy changes related to the amount of AP (OR: 5.4, 95% CI: 1.5, 20). Higher self-efficacy was associated with a higher odds of policy changes related to outdoor AP (OR = 2.9, 95% 1.1, 7.8). Appetite to Play training was a positively associated with practice changes related to FMS (ß = 0.5, 95% CI: 0.1, 0.9). CONCLUSIONS: A hierarchy of theoretically defined factors influenced childcare providers' implementation of the AP Standards in BC. Future research should test the feasibility of modifying these factors to improve the implementation of PA policy and practice interventions in this setting.

Dissemination of public health research to prevent non-communicable diseases: a scoping review.

BACKGROUND: Dissemination is a critical element of the knowledge translation pathway, and a necessary step to ensure research evidence is adopted and implemented by key end users in order to improve health outcomes. However, evidence-based guidance to inform dissemination activities in research is limited. This scoping review aimed to identify and describe the scientific literature examining strategies to disseminate public health evidence related to the prevention of non-communicable diseases. METHODS: Medline, PsycInfo and EBSCO Search Ultimate were searched in May 2021 for studies published between January 2000 and the search date that reported on the dissemination of evidence to end users of public health evidence, within the context of the prevention of non-communicable diseases. Studies were synthesised according to the four components of Brownson and colleagues' Model for Dissemination of Research (source, message, channel and audience), as well as by study design. RESULTS: Of the 107 included studies, only 14% (n = 15) directly tested dissemination strategies using experimental designs. The remainder primarily reported on dissemination preferences of different populations, or outcomes such as awareness, knowledge and intentions to adopt following evidence dissemination. Evidence related to diet, physical activity and/or obesity prevention was the most disseminated topic. Researchers were the source of disseminated evidence in over half the studies, and study findings/knowledge summaries were more frequently disseminated as the message compared to guidelines or an evidence-based program/intervention. A broad range of dissemination channels were utilised, although peer-reviewed publications/conferences and presentations/workshops predominated. Practitioners were the most commonly reported target audience. CONCLUSIONS: There is a significant gap in the peer reviewed literature, with few experimental studies published that analyse and evaluate the effect of different sources, messages and target audiences on the determinants of uptake of public health evidence for prevention. Such studies are important as they can help inform and improve the effectiveness of current and future dissemination practices in public health contexts.

A cluster randomised controlled trial to assess the effectiveness of a multi-strategy sustainability intervention on teachers' sustained implementation of classroom physical activity breaks (energisers): study protocol.

BACKGROUND: Governments internationally have invested hugely in the implementation and scale-up of school-based physical activity interventions, but have little evidence of how to best sustain these interventions once active implementation support ceases. This study will assess the effectiveness of a multi-strategy sustainability intervention on classroom teachers' sustainment of energisers (short 3-5 min physical activity breaks during class-time) scheduled across the school day from baseline to 12 and 24-month follow-up. METHODS: A cluster randomised controlled trial will be conducted in 50 primary schools within the Hunter New England, Illawarra Shoalhaven, Murrumbidgee and Northern New South Wales (NSW) Local Health Districts of NSW Australia. Schools will be randomly allocated to receive either usual support or the multi-strategy sustainability intervention that includes: centralised technical assistance from a trained project officer; formal commitment and mandated change obtained from school principals; training in-school champions; reminders for teachers; educational materials provided to teachers; capturing and sharing local knowledge; and engagement of parents, carers and the wider school community. The primary trial outcome will be measured via a teacher logbook to determine the between-group difference in the change in mean minutes of energisers scheduled across the school day at 12 and 24-month follow-up compared to baseline. Analyses will be performed using an intention to treat framework. Linear mixed models will be used to assess intervention effects on the primary outcome at both follow-up periods. DISCUSSION: This study will be one of the first randomised controlled trials to examine the impact of a multi-strategy sustainability intervention to support schools' sustainment of a physical activity intervention. The proposed research will generate new evidence needed for the partnering organisations to protect their considerable investments to date in physical activity promotion in this setting and will provide seminal evidence for the field globally. TRIAL REGISTRATION: ACTRN12620000372987 version 1 registered 17th March 2020. Version 3 (current version) updated 4th August 2023.

Exploratory analysis of a cluster randomized controlled trial of a multi-strategy intervention delivered via online canteens on improving the nutritional quality of primary school students' pre-ordered foods & drinks at recess.

School canteens are a recommended setting to deliver public health nutrition strategies given their wide reach, and frequent use by children. Online canteens, where users (i.e. students or their carers) pre-order and pay for food and drinks online, represent attractive systems to deliver strategies that encourage healthier food choices. There have been few studies exploring the efficacy of public health nutrition interventions in online food ordering environments. Therefore, this study aims to assess the efficacy of a multi-strategy intervention implemented in an online school canteen ordering system in reducing the energy, saturated fat, sugar, and sodium content of students' online recess orders (i.e. foods ordered during the mid-morning or afternoon snack period). This was an exploratory analysis of recess purchases for a cluster randomized controlled trial that initially sought to examine the efficacy of the intervention on lunch orders. A total of 314 students from 5 schools received the multi-strategy intervention (involving menu labelling, placement, prompting and availability) that was integrated into the online ordering system, and 171 students from 3 schools received the control (usual online ordering). Analysis of main outcomes found that the mean energy (difference: -269.3 kJ; P = 0.006), saturated fat (difference: -1.1 g; P = 0.011) and sodium (difference: -128.6 mg; P = 0.014) content per student recess order was significantly lower in the intervention group than the control group at 2-month follow-up. Findings suggest that embedding strategies to encourage healthier choices within online canteen ordering systems can improve the nutrient composition of student recess purchases. These results add to the current evidence base suggesting that interventions delivered via online food ordering systems represent an effective strategy for improving child public health nutrition in schools.