Caregiver involvement in interventions for improving children's dietary intake and physical activity behaviors.

Poor diet and insufficient physical activity are major risk factors for non-communicable diseases. Developing healthy diet and physical activity behaviors early in life is important as these behaviors track between childhood and adulthood. Parents and other adult caregivers have important influences on children's health behaviors, but whether their involvement in children's nutrition and physical activity interventions contributes to intervention effectiveness is not known. • To assess effects of caregiver involvement in interventions for improving children's dietary intake and physical activity behaviors, including those intended to prevent overweight and obesity • To describe intervention content and behavior change techniques employed, drawing from a behavior change technique taxonomy developed and advanced by Abraham, Michie, and colleagues (Abraham 2008; Michie 2011; Michie 2013; Michie 2015) • To identify content and techniques related to reported outcomes when such information was reported in included studies SEARCH METHODS: In January 2019, we searched CENTRAL, MEDLINE, Embase, 11 other databases, and three trials registers. We also searched the references lists of relevant reports and systematic reviews. Randomised controlled trials (RCTs) and quasi-RCTs evaluating the effects of interventions to improve children's dietary intake or physical activity behavior, or both, with children aged 2 to 18 years as active participants and at least one component involving caregivers versus the same interventions but without the caregiver component(s). We excluded interventions meant as treatment or targeting children with pre-existing conditions, as well as caregiver-child units residing in orphanages and school hostel environments. We used standard methodological procedures outlined by Cochrane. We included 23 trials with approximately 12,192 children in eligible intervention arms. With the exception of two studies, all were conducted in high-income countries, with more than half performed in North America. Most studies were school-based and involved the addition of healthy eating or physical education classes, or both, sometimes in tandem with other changes to the school environment. The specific intervention strategies used were not always reported completely. However, based on available reports, the behavior change techniques used most commonly in the child-only arm were "shaping knowledge," "comparison of behavior," "feedback and monitoring," and "repetition and substitution." In the child + caregiver arm, the strategies used most commonly included additional "shaping knowledge" or "feedback and monitoring" techniques, as well as "social support" and "natural consequences." We considered all trials to be at high risk of bias for at least one design factor. Seven trials did not contribute any data to analyses. The quality of reporting of intervention content varied between studies, and there was limited scope for meta-analysis. Both validated and non-validated instruments were used to measure outcomes of interest. Outcomes measured and reported differed between studies, with 16 studies contributing data to the meta-analyses. About three-quarters of studies reported their funding sources; no studies reported industry funding. We assessed the quality of evidence to be low or very low. Dietary behavior change interventions with a caregiver component versus interventions without a caregiver component Seven studies compared dietary behavior change interventions with and without a caregiver component. At the end of the intervention, we did not detect a difference between intervention arms in children's percentage of total energy intake from saturated fat (mean difference [MD] -0.42%, 95% confidence interval [CI] -1.25 to 0.41, 1 study, n = 207; low-quality evidence) or from sodium intake (MD -0.12 g/d, 95% CI -0.36 to 0.12, 1 study, n = 207; low-quality evidence). No trial in this comparison reported data for children's combined fruit and vegetable intake, sugar-sweetened beverage (SSB) intake, or physical activity levels, nor for adverse effects of interventions. Physical activity interventions with a caregiver component versus interventions without a caregiver component Six studies compared physical activity interventions with and without a caregiver component. At the end of the intervention, we did not detect a difference between intervention arms in children's total physical activity (MD 0.20 min/h, 95% CI -1.19 to 1.59, 1 study, n = 54; low-quality evidence) or moderate to vigorous physical activity (MVPA) (standard mean difference [SMD] 0.04, 95% CI -0.41 to 0.49, 2 studies, n = 80; moderate-quality evidence). No trial in this comparison reported data for percentage of children's total energy intake from saturated fat, sodium intake, fruit and vegetable intake, or SSB intake, nor for adverse effects of interventions. Combined dietary and physical activity interventions with a caregiver component versus interventions without a caregiver component Ten studies compared dietary and physical activity interventions with and without a caregiver component. At the end of the intervention, we detected a small positive impact of a caregiver component on children's SSB intake (SMD -0.28, 95% CI -0.44 to -0.12, 3 studies, n = 651; moderate-quality evidence). We did not detect a difference between intervention arms in children's percentage of total energy intake from saturated fat (MD 0.06%, 95% CI -0.67 to 0.80, 2 studies, n = 216; very low-quality evidence), sodium intake (MD 35.94 mg/d, 95% CI -322.60 to 394.47, 2 studies, n = 315; very low-quality evidence), fruit and vegetable intake (MD 0.38 servings/d, 95% CI -0.51 to 1.27, 1 study, n = 134; very low-quality evidence), total physical activity (MD 1.81 min/d, 95% CI -15.18 to 18.80, 2 studies, n = 573; low-quality evidence), or MVPA (MD -0.05 min/d, 95% CI -18.57 to 18.47, 1 study, n = 622; very low-quality evidence). One trial indicated that no adverse events were reported by study participants but did not provide data. Current evidence is insufficient to support the inclusion of caregiver involvement in interventions to improve children's dietary intake or physical activity behavior, or both. For most outcomes, the quality of the evidence is adversely impacted by the small number of studies with available data, limited effective sample sizes, risk of bias, and imprecision. To establish the value of caregiver involvement, additional studies measuring clinically important outcomes using valid and reliable measures, employing appropriate design and power, and following established reporting guidelines are needed, as is evidence on how such interventions might contribute to health equity.

Morgan EH ，Schoonees A ，Sriram U ，Faure M ，Seguin-Fowler RA ... -  《Cochrane Database of Systematic Reviews》

Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.

Description of the condition Malaria, an infectious disease transmitted by the bite of female mosquitoes from several Anopheles species, occurs in 87 countries with ongoing transmission (WHO 2020). The World Health Organization (WHO) estimated that, in 2019, approximately 229 million cases of malaria occurred worldwide, with 94% occurring in the WHO's African region (WHO 2020). Of these malaria cases, an estimated 409,000 deaths occurred globally, with 67% occurring in children under five years of age (WHO 2020). Malaria also negatively impacts the health of women during pregnancy, childbirth, and the postnatal period (WHO 2020). Sulfadoxine/pyrimethamine (SP), an antifolate antimalarial, has been widely used across sub-Saharan Africa as the first-line treatment for uncomplicated malaria since it was first introduced in Malawi in 1993 (Filler 2006). Due to increasing resistance to SP, in 2000 the WHO recommended that one of several artemisinin-based combination therapies (ACTs) be used instead of SP for the treatment of uncomplicated malaria caused by Plasmodium falciparum (Global Partnership to Roll Back Malaria 2001). However, despite these recommendations, SP continues to be advised for intermittent preventive treatment in pregnancy (IPTp) and intermittent preventive treatment in infants (IPTi), whether the person has malaria or not (WHO 2013). Description of the intervention Folate (vitamin B9) includes both naturally occurring folates and folic acid, the fully oxidized monoglutamic form of the vitamin, used in dietary supplements and fortified food. Folate deficiency (e.g. red blood cell (RBC) folate concentrations of less than 305 nanomoles per litre (nmol/L); serum or plasma concentrations of less than 7 nmol/L) is common in many parts of the world and often presents as megaloblastic anaemia, resulting from inadequate intake, increased requirements, reduced absorption, or abnormal metabolism of folate (Bailey 2015; WHO 2015a). Pregnant women have greater folate requirements; inadequate folate intake (evidenced by RBC folate concentrations of less than 400 nanograms per millilitre (ng/mL), or 906 nmol/L) prior to and during the first month of pregnancy increases the risk of neural tube defects, preterm delivery, low birthweight, and fetal growth restriction (Bourassa 2019). The WHO recommends that all women who are trying to conceive consume 400 micrograms (µg) of folic acid daily from the time they begin trying to conceive through to 12 weeks of gestation (WHO 2017). In 2015, the WHO added the dosage of 0.4 mg of folic acid to the essential drug list (WHO 2015c). Alongside daily oral iron (30 mg to 60 mg elemental iron), folic acid supplementation is recommended for pregnant women to prevent neural tube defects, maternal anaemia, puerperal sepsis, low birthweight, and preterm birth in settings where anaemia in pregnant women is a severe public health problem (i.e. where at least 40% of pregnant women have a blood haemoglobin (Hb) concentration of less than 110 g/L). How the intervention might work Potential interactions between folate status and malaria infection The malaria parasite requires folate for survival and growth; this has led to the hypothesis that folate status may influence malaria risk and severity. In rhesus monkeys, folate deficiency has been found to be protective against Plasmodium cynomolgi malaria infection, compared to folate-replete animals (Metz 2007). Alternatively, malaria may induce or exacerbate folate deficiency due to increased folate utilization from haemolysis and fever. Further, folate status measured via RBC folate is not an appropriate biomarker of folate status in malaria-infected individuals since RBC folate values in these individuals are indicative of both the person's stores and the parasite's folate synthesis. A study in Nigeria found that children with malaria infection had significantly higher RBC folate concentrations compared to children without malaria infection, but plasma folate levels were similar (Bradley-Moore 1985). Why it is important to do this review The malaria parasite needs folate for survival and growth in humans. For individuals, adequate folate levels are critical for health and well-being, and for the prevention of anaemia and neural tube defects. Many countries rely on folic acid supplementation to ensure adequate folate status in at-risk populations. Different formulations for folic acid supplements are available in many international settings, with dosages ranging from 400 µg to 5 mg. Evaluating folic acid dosage levels used in supplementation efforts may increase public health understanding of its potential impacts on malaria risk and severity and on treatment failures. Examining folic acid interactions with antifolate antimalarial medications and with malaria disease progression may help countries in malaria-endemic areas determine what are the most appropriate lower dose folic acid formulations for at-risk populations. The WHO has highlighted the limited evidence available and has indicated the need for further research on biomarkers of folate status, particularly interactions between RBC folate concentrations and tuberculosis, human immunodeficiency virus (HIV), and antifolate antimalarial drugs (WHO 2015b). An earlier Cochrane Review assessed the effects and safety of iron supplementation, with or without folic acid, in children living in hyperendemic or holoendemic malaria areas; it demonstrated that iron supplementation did not increase the risk of malaria, as indicated by fever and the presence of parasites in the blood (Neuberger 2016). Further, this review stated that folic acid may interfere with the efficacy of SP; however, the efficacy and safety of folic acid supplementation on these outcomes has not been established. This review will provide evidence on the effectiveness of daily folic acid supplementation in healthy and malaria-infected individuals living in malaria-endemic areas. Additionally, it will contribute to achieving both the WHO Global Technical Strategy for Malaria 2016-2030 (WHO 2015d), and United Nations Sustainable Development Goal 3 (to ensure healthy lives and to promote well-being for all of all ages) (United Nations 2021), and evaluating whether the potential effects of folic acid supplementation, at different doses (e.g. 0.4 mg, 1 mg, 5 mg daily), interferes with the effect of drugs used for prevention or treatment of malaria. To examine the effects of folic acid supplementation, at various doses, on malaria susceptibility (risk of infection) and severity among people living in areas with various degrees of malaria endemicity. We will examine the interaction between folic acid supplements and antifolate antimalarial drugs. Specifically, we will aim to answer the following. Among uninfected people living in malaria endemic areas, who are taking or not taking antifolate antimalarials for malaria prophylaxis, does taking a folic acid-containing supplement increase susceptibility to or severity of malaria infection? Among people with malaria infection who are being treated with antifolate antimalarials, does folic acid supplementation increase the risk of treatment failure? Criteria for considering studies for this review Types of studies Inclusion criteria Randomized controlled trials (RCTs) Quasi-RCTs with randomization at the individual or cluster level conducted in malaria-endemic areas (areas with ongoing, local malaria transmission, including areas approaching elimination, as listed in the World Malaria Report 2020) (WHO 2020) Exclusion criteria Ecological studies Observational studies In vivo/in vitro studies Economic studies Systematic literature reviews and meta-analyses (relevant systematic literature reviews and meta-analyses will be excluded but flagged for grey literature screening) Types of participants Inclusion criteria Individuals of any age or gender, living in a malaria endemic area, who are taking antifolate antimalarial medications (including but not limited to sulfadoxine/pyrimethamine (SP), pyrimethamine-dapsone, pyrimethamine, chloroquine and proguanil, cotrimoxazole) for the prevention or treatment of malaria (studies will be included if more than 70% of the participants live in malaria-endemic regions) Studies assessing participants with or without anaemia and with or without malaria parasitaemia at baseline will be included Exclusion criteria Individuals not taking antifolate antimalarial medications for prevention or treatment of malaria Individuals living in non-malaria endemic areas Types of interventions Inclusion criteria Folic acid supplementation Form: in tablet, capsule, dispersible tablet at any dose, during administration, or periodically Timing: during, before, or after (within a period of four to six weeks) administration of antifolate antimalarials Iron-folic acid supplementation Folic acid supplementation in combination with co-interventions that are identical between the intervention and control groups. Co-interventions include: anthelminthic treatment; multivitamin or multiple micronutrient supplementation; 5-methyltetrahydrofolate supplementation. Exclusion criteria Folate through folate-fortified water Folic acid administered through large-scale fortification of rice, wheat, or maize Comparators Placebo No treatment No folic acid/different doses of folic acid Iron Types of outcome measures Primary outcomes Uncomplicated malaria (defined as a history of fever with parasitological confirmation; acceptable parasitological confirmation will include rapid diagnostic tests (RDTs), malaria smears, or nucleic acid detection (i.e. polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), etc.)) (WHO 2010). This outcome is relevant for patients without malaria, given antifolate antimalarials for malaria prophylaxis. Severe malaria (defined as any case with cerebral malaria or acute P. falciparum malaria, with signs of severity or evidence of vital organ dysfunction, or both) (WHO 2010). This outcome is relevant for patients without malaria, given antifolate antimalarials for malaria prophylaxis. Parasite clearance (any Plasmodium species), defined as the time it takes for a patient who tests positive at enrolment and is treated to become smear-negative or PCR negative. This outcome is relevant for patients with malaria, treated with antifolate antimalarials. Treatment failure (defined as the inability to clear malaria parasitaemia or prevent recrudescence after administration of antimalarial medicine, regardless of whether clinical symptoms are resolved) (WHO 2019). This outcome is relevant for patients with malaria, treated with antifolate antimalarials. Secondary outcomes Duration of parasitaemia Parasite density Haemoglobin (Hb) concentrations (g/L) Anaemia: severe anaemia (defined as Hb less than 70 g/L in pregnant women and children aged six to 59 months; and Hb less than 80 g/L in other populations); moderate anaemia (defined as Hb less than 100 g/L in pregnant women and children aged six to 59 months; and less than 110 g/L in others) Death from any cause Among pregnant women: stillbirth (at less than 28 weeks gestation); low birthweight (less than 2500 g); active placental malaria (defined as Plasmodium detected in placental blood by smear or PCR, or by Plasmodium detected on impression smear or placental histology). Search methods for identification of studies A search will be conducted to identify completed and ongoing studies, without date or language restrictions. Electronic searches A search strategy will be designed to include the appropriate subject headings and text word terms related to each intervention of interest and study design of interest (see Appendix 1). Searches will be broken down by these two criteria (intervention of interest and study design of interest) to allow for ease of prioritization, if necessary. The study design filters recommended by the Scottish Intercollegiate Guidelines Network (SIGN), and those designed by Cochrane for identifying clinical trials for MEDLINE and Embase, will be used (SIGN 2020). There will be no date or language restrictions. Non-English articles identified for inclusion will be translated into English. If translations are not possible, advice will be requested from the Cochrane Infectious Diseases Group and the record will be stored in the "Awaiting assessment" section of the review until a translation is available. The following electronic databases will be searched for primary studies. Cochrane Central Register of Controlled Trials. Cumulative Index to Nursing and Allied Health Literature (CINAHL). Embase. MEDLINE. Scopus. Web of Science (both the Social Science Citation Index and the Science Citation Index). We will conduct manual searches of ClinicalTrials.gov, the International Clinical Trials Registry Platform (ICTRP), and the United Nations Children's Fund (UNICEF) Evaluation and Research Database (ERD), in order to identify relevant ongoing or planned trials, abstracts, and full-text reports of evaluations, studies, and surveys related to programmes on folic acid supplementation in malaria-endemic areas. Additionally, manual searches of grey literature to identify RCTs that have not yet been published but are potentially eligible for inclusion will be conducted in the following sources. Global Index Medicus (GIM). African Index Medicus (AIM). Index Medicus for the Eastern Mediterranean Region (IMEMR). Latin American & Caribbean Health Sciences Literature (LILACS). Pan American Health Organization (PAHO). Western Pacific Region Index Medicus (WPRO). Index Medicus for the South-East Asian Region (IMSEAR). The Spanish Bibliographic Index in Health Sciences (IBECS) (ibecs.isciii.es/). Indian Journal of Medical Research (IJMR) (journals.lww.com/ijmr/pages/default.aspx). Native Health Database (nativehealthdatabase.net/). Scielo (www.scielo.br/). Searching other resources Handsearches of the five journals with the highest number of included studies in the last 12 months will be conducted to capture any relevant articles that may not have been indexed in the databases at the time of the search. We will contact the authors of included studies and will check reference lists of included papers for the identification of additional records. For assistance in identifying ongoing or unpublished studies, we will contact the Division of Nutrition, Physical Activity, and Obesity (DNPAO) and the Division of Parasitic Diseases and Malaria (DPDM) of the CDC, the United Nations World Food Programme (WFP), Nutrition International (NI), Global Alliance for Improved Nutrition (GAIN), and Hellen Keller International (HKI). Data collection and analysis Selection of studies Two review authors will independently screen the titles and abstracts of articles retrieved by each search to assess eligibility, as determined by the inclusion and exclusion criteria. Studies deemed eligible for inclusion by both review authors in the abstract screening phase will advance to the full-text screening phase, and full-text copies of all eligible papers will be retrieved. If full articles cannot be obtained, we will attempt to contact the authors to obtain further details of the studies. If such information is not obtained, we will classify the study as "awaiting assessment" until further information is published or made available to us. The same two review authors will independently assess the eligibility of full-text articles for inclusion in the systematic review. If any discrepancies occur between the studies selected by the two review authors, a third review author will provide arbitration. Each trial will be scrutinized to identify multiple publications from the same data set, and the justification for excluded trials will be documented. A PRISMA flow diagram of the study selection process will be presented to provide information on the number of records identified in the literature searches, the number of studies included and excluded, and the reasons for exclusion (Moher 2009). The list of excluded studies, along with their reasons for exclusion at the full-text screening phase, will also be created. Data extraction and management Two review authors will independently extract data for the final list of included studies using a standardized data specification form. Discrepancies observed between the data extracted by the two authors will be resolved by involving a third review author and reaching a consensus. Information will be extracted on study design components, baseline participant characteristics, intervention characteristics, and outcomes. For individually randomized trials, we will record the number of participants experiencing the event and the number analyzed in each treatment group or the effect estimate reported (e.g. risk ratio (RR)) for dichotomous outcome measures. For count data, we will record the number of events and the number of person-months of follow-up in each group. If the number of person-months is not reported, the product of the duration of follow-up and the number of children evaluated will be used to estimate this figure. We will calculate the rate ratio and standard error (SE) for each study. Zero events will be replaced by 0.5. We will extract both adjusted and unadjusted covariate incidence rate ratios if they are reported in the original studies. For continuous data, we will extract means (arithmetic or geometric) and a measure of variance (standard deviation (SD), SE, or confidence interval (CI)), percentage or mean change from baseline, and the numbers analyzed in each group. SDs will be computed from SEs or 95% CIs, assuming a normal distribution of the values. Haemoglobin values in g/dL will be calculated by multiplying haematocrit or packed cell volume values by 0.34, and studies reporting haemoglobin values in g/dL will be converted to g/L. In cluster-randomized trials, we will record the unit of randomization (e.g. household, compound, sector, or village), the number of clusters in the trial, and the average cluster size. The statistical methods used to analyze the trials will be documented, along with details describing whether these methods adjusted for clustering or other covariates. We plan to extract estimates of the intra-cluster correlation coefficient (ICC) for each outcome. Where results are adjusted for clustering, we will extract the treatment effect estimate and the SD or CI. If the results are not adjusted for clustering, we will extract the data reported. Assessment of risk of bias in included studies Two review authors (KSC, LFY) will independently assess the risk of bias for each included trial using the Cochrane 'Risk of bias 2' tool (RoB 2) for randomized studies (Sterne 2019). Judgements about the risk of bias of included studies will be made according to the recommendations outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). Disagreements will be resolved by discussion, or by involving a third review author. The interest of our review will be to assess the effect of assignment to the interventions at baseline. We will evaluate each primary outcome using the RoB2 tool. The five domains of the Cochrane RoB2 tool include the following. Bias arising from the randomization process. Bias due to deviations from intended interventions. Bias due to missing outcome data. Bias in measurement of the outcome. Bias in selection of the reported result. Each domain of the RoB2 tool comprises the following. A series of 'signalling' questions. A judgement about the risk of bias for the domain, facilitated by an algorithm that maps responses to the signalling questions to a proposed judgement. Free-text boxes to justify responses to the signalling questions and 'Risk of bias' judgements. An option to predict (and explain) the likely direction of bias. Responses to signalling questions elicit information relevant to an assessment of the risk of bias. These response options are as follows. Yes (may indicate either low or high risk of bias, depending on the most natural way to ask the question). Probably yes. Probably no. No. No information (may indicate no evidence of that problem or an absence of information leading to concerns about there being a problem). Based on the answer to the signalling question, a 'Risk of bias' judgement is assigned to each domain. These judgements include one of the following. High risk of bias Low risk of bias Some concerns To generate the risk of bias judgement for each domain in the randomized studies, we will use the Excel template, available at www.riskofbias.info/welcome/rob-2-0-tool/current-version-of-rob-2. This file will be stored on a scientific data website, available to readers. Risk of bias in cluster randomized controlled trials For the cluster randomized trials, we will be using the RoB2 tool to analyze the five standard domains listed above along with Domain 1b (bias arising from the timing of identification or recruitment of participants) and its related signalling questions. To generate the risk of bias judgement for each domain in the cluster RCTs, we will use the Excel template available at https://sites.google.com/site/riskofbiastool/welcome/rob-2-0-tool/rob-2-for-cluster-randomized-trials. This file will be stored on a scientific data website, available to readers. Risk of bias in cross-over randomized controlled trials For cross-over randomized trials, we will be using the RoB2 tool to analyze the five standard domains listed above along with Domain 2 (bias due to deviations from intended interventions), and Domain 3 (bias due to missing outcome data), and their respective signalling questions. To generate the risk of bias judgement for each domain in the cross-over RCTs, we will use the Excel template, available at https://sites.google.com/site/riskofbiastool/welcome/rob-2-0-tool/rob-2-for-crossover-trials, for each risk of bias judgement of cross-over randomized studies. This file will be stored on a scientific data website, available to readers. Overall risk of bias The overall 'Risk of bias' judgement for each specific trial being assessed will be based on each domain-level judgement. The overall judgements include the following. Low risk of bias (the trial is judged to be at low risk of bias for all domains). Some concerns (the trial is judged to raise some concerns in at least one domain but is not judged to be at high risk of bias for any domain). High risk of bias (the trial is judged to be at high risk of bias in at least one domain, or is judged to have some concerns for multiple domains in a way that substantially lowers confidence in the result). The 'risk of bias' assessments will inform our GRADE evaluations of the certainty of evidence for our primary outcomes presented in the 'Summary of findings' tables and will also be used to inform the sensitivity analyses; (see Sensitivity analysis). If there is insufficient information in study reports to enable an assessment of the risk of bias, studies will be classified as "awaiting assessment" until further information is published or made available to us. Measures of treatment effect Dichotomous data For dichotomous data, we will present proportions and, for two-group comparisons, results as average RR or odds ratio (OR) with 95% CIs. Ordered categorical data Continuous data We will report results for continuous outcomes as the mean difference (MD) with 95% CIs, if outcomes are measured in the same way between trials. Where some studies have reported endpoint data and others have reported change-from-baseline data (with errors), we will combine these in the meta-analysis, if the outcomes were reported using the same scale. We will use the standardized mean difference (SMD), with 95% CIs, to combine trials that measured the same outcome but used different methods. If we do not find three or more studies for a pooled analysis, we will summarize the results in a narrative form. Unit of analysis issues Cluster-randomized trials We plan to combine results from both cluster-randomized and individually randomized studies, providing there is little heterogeneity between the studies. If the authors of cluster-randomized trials conducted their analyses at a different level from that of allocation, and they have not appropriately accounted for the cluster design in their analyses, we will calculate the trials' effective sample sizes to account for the effect of clustering in data. When one or more cluster-RCT reports RRs adjusted for clustering, we will compute cluster-adjusted SEs for the other trials. When none of the cluster-RCTs provide cluster-adjusted RRs, we will adjust the sample size for clustering. We will divide, by the estimated design effects (DE), the number of events and number evaluated for dichotomous outcomes and the number evaluated for continuous outcomes, where DE = 1 + ((average cluster size 1) * ICC). The derivation of the estimated ICCs and DEs will be reported. We will utilize the intra-cluster correlation coefficient (ICC), derived from the trial (if available), or from another source (e.g., using the ICCs derived from other, similar trials) and then calculate the design effect with the formula provided in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). If this approach is used, we will report it and undertake sensitivity analysis to investigate the effect of variations in ICC. Studies with more than two treatment groups If we identify studies with more than two intervention groups (multi-arm studies), where possible we will combine groups to create a single pair-wise comparison or use the methods set out in the Cochrane Handbook to avoid double counting study participants (Higgins 2021). For the subgroup analyses, when the control group was shared by two or more study arms, we will divide the control group (events and total population) over the number of relevant subgroups to avoid double counting the participants. Trials with several study arms can be included more than once for different comparisons. Cross-over trials From cross-over trials, we will consider the first period of measurement only and will analyze the results together with parallel-group studies. Multiple outcome events In several outcomes, a participant might experience more than one outcome event during the trial period. For all outcomes, we will extract the number of participants with at least one event. Dealing with missing data We will contact the trial authors if the available data are unclear, missing, or reported in a format that is different from the format needed. We aim to perform a 'per protocol' or 'as observed' analysis; otherwise, we will perform a complete case analysis. This means that for treatment failure, we will base the analyses on the participants who received treatment and the number of participants for which there was an inability to clear malarial parasitaemia or prevent recrudescence after administration of an antimalarial medicine reported in the studies. Assessment of heterogeneity Heterogeneity in the results of the trials will be assessed by visually examining the forest plot to detect non-overlapping CIs, using the Chi2 test of heterogeneity (where a P value of less than 0.1 indicates statistical significance) and the I2 statistic of inconsistency (with a value of greater than 50% denoting moderate levels of heterogeneity). When statistical heterogeneity is present, we will investigate the reasons for it, using subgroup analysis. Assessment of reporting biases We will construct a funnel plot to assess the effect of small studies for the main outcome (when including more than 10 trials). Data synthesis The primary analysis will include all eligible studies that provide data regardless of the overall risk of bias as assessed by the RoB2 tool. Analyses will be conducted using Review Manager 5.4 (Review Manager 2020). Cluster-RCTs will be included in the main analysis after adjustment for clustering (see the previous section on cluster-RCTs). The meta-analysis will be performed using the Mantel-Haenszel random-effects model or the generic inverse variance method (when adjustment for clustering is performed by adjusting SEs), as appropriate. Subgroup analysis and investigation of heterogeneity The overall risk of bias will not be used as the basis in conducting our subgroup analyses. However, where data are available, we plan to conduct the following subgroup analyses, independent of heterogeneity. Dose of folic acid supplementation: higher doses (4 mg or more, daily) versus lower doses (less than 4 mg, daily). Moderate-severe anaemia at baseline (mean haemoglobin of participants in a trial at baseline below 100 g/L for pregnant women and children aged six to 59 months, and below 110 g/L for other populations) versus normal at baseline (mean haemoglobin above 100 g/L for pregnant women and children aged six to 59 months, and above 110 g/L for other populations). Antimalarial drug resistance to parasite: known resistance versus no resistance versus unknown/mixed/unreported parasite resistance. Folate status at baseline: Deficient (e.g. RBC folate concentration of less than 305 nmol/L, or serum folate concentration of less than 7nmol/L) and Insufficient (e.g. RBC folate concentration from 305 to less than 906 nmol/L, or serum folate concentration from 7 to less than 25 nmol/L) versus Sufficient (e.g. RBC folate concentration above 906 nmol/L, or serum folate concentration above 25 nmol/L). Presence of anaemia at baseline: yes versus no. Mandatory fortification status: yes, versus no (voluntary or none). We will only use the primary outcomes in any subgroup analyses, and we will limit subgroup analyses to those outcomes for which three or more trials contributed data. Comparisons between subgroups will be performed using Review Manager 5.4 (Review Manager 2020). Sensitivity analysis We will perform a sensitivity analysis, using the risk of bias as a variable to explore the robustness of the findings in our primary outcomes. We will verify the behaviour of our estimators by adding and removing studies with a high risk of bias overall from the analysis. That is, studies with a low risk of bias versus studies with a high risk of bias. Summary of findings and assessment of the certainty of the evidence For the assessment across studies, we will use the GRADE approach, as outlined in (Schünemann 2021). We will use the five GRADE considerations (study limitations based on RoB2 judgements, consistency of effect, imprecision, indirectness, and publication bias) to assess the certainty of the body of evidence as it relates to the studies which contribute data to the meta-analyses for the primary outcomes. The GRADEpro Guideline Development Tool (GRADEpro) will be used to import data from Review Manager 5.4 (Review Manager 2020) to create 'Summary of Findings' tables. The primary outcomes for the main comparison will be listed with estimates of relative effects, along with the number of participants and studies contributing data for those outcomes. These tables will provide outcome-specific information concerning the overall certainty of evidence from studies included in the comparison, the magnitude of the effect of the interventions examined, and the sum of available data on the outcomes we considered. We will include only primary outcomes in the summary of findings tables. For each individual outcome, two review authors (KSC, LFY) will independently assess the certainty of the evidence using the GRADE approach (Balshem 2011). For assessments of the overall certainty of evidence for each outcome that includes pooled data from included trials, we will downgrade the evidence from 'high certainty' by one level for serious (or by two for very serious) study limitations (risk of bias, indirectness of evidence, serious inconsistency, imprecision of effect estimates, or potential publication bias).

Crider K ，Williams J ，Qi YP ，Gutman J ，Yeung L ，Mai C ，Finkelstain J ，Mehta S ，Pons-Duran C ，Menéndez C ，Moraleda C ，Rogers L ，Daniels K ，Green P ... -  《Cochrane Database of Systematic Reviews》

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

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. 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. 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. 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. 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. 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). 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.

Yoong SL ，Lum M ，Wolfenden L ，Jackson J ，Barnes C ，Hall AE ，McCrabb S ，Pearson N ，Lane C ，Jones JZ ，Nolan E ，Dinour L ，McDonnell T ，Booth D ，Grady A ... -  《Cochrane Database of Systematic Reviews》

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

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. 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. 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. 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. 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). 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.

Yoong SL ，Lum M ，Wolfenden L ，Jackson J ，Barnes C ，Hall AE ，McCrabb S ，Pearson N ，Lane C ，Jones JZ ，Dinour L ，McDonnell T ，Booth D ，Grady A ... -  《Cochrane Database of Systematic Reviews》

Interventions for increasing fruit and vegetable consumption in children aged five years and under.

Insufficient consumption of fruits and vegetables in childhood increases the risk of future non-communicable diseases, including cardiovascular disease. Testing the effects of interventions to increase consumption of fruit and vegetables, including those focused on specific child-feeding strategies or broader multicomponent interventions targeting the home or childcare environment is required to assess the potential to reduce this disease burden. To assess the effectiveness, cost effectiveness and associated adverse events of interventions designed to increase the consumption of fruit, vegetables or both amongst children aged five years and under. We searched CENTRAL, MEDLINE, Embase and two clinical trials registries to identify eligible trials on 25 January 2020. We searched Proquest Dissertations and Theses in November 2019. We reviewed reference lists of included trials and handsearched three international nutrition journals. We contacted authors of included trials to identify further potentially relevant trials. We included randomised controlled trials, including cluster-randomised controlled trials and cross-over trials, of any intervention primarily targeting consumption of fruit, vegetables or both among children aged five years and under, and incorporating a dietary or biochemical assessment of fruit or vegetable consumption. Two review authors independently screened titles and abstracts of identified papers; a third review author resolved disagreements. Two review authors independently extracted data and assessed the risks of bias of included trials; a third review author resolved disagreements. Due to unexplained heterogeneity, we used random-effects models in meta-analyses for the primary review outcomes where we identified sufficient trials. We calculated standardised mean differences (SMDs) to account for the heterogeneity of fruit and vegetable consumption measures. We conducted assessments of risks of bias and evaluated the quality of evidence (GRADE approach) using Cochrane procedures. We included 80 trials with 218 trial arms and 12,965 participants. Fifty trials examined the impact of child-feeding practices (e.g. repeated food exposure) in increasing child vegetable intake. Fifteen trials examined the impact of parent nutrition education only in increasing child fruit and vegetable intake. Fourteen trials examined the impact of multicomponent interventions (e.g. parent nutrition education and preschool policy changes) in increasing child fruit and vegetable intake. Two trials examined the effect of a nutrition education intervention delivered to children in increasing child fruit and vegetable intake. One trial examined the impact of a child-focused mindfulness intervention in increasing vegetable intake. We judged 23 of the 80 included trials as free from high risks of bias across all domains. Performance, detection and attrition bias were the most common domains judged at high risk of bias for the remaining trials. There is low-quality evidence that child-feeding practices versus no intervention may have a small positive effect on child vegetable consumption, equivalent to an increase of 5.30 grams as-desired consumption of vegetables (SMD 0.50, 95% CI 0.29 to 0.71; 19 trials, 2140 participants; mean post-intervention follow-up = 8.3 weeks). Multicomponent interventions versus no intervention has a small effect on child consumption of fruit and vegetables (SMD 0.32, 95% CI 0.09 to 0.55; 9 trials, 2961 participants; moderate-quality evidence; mean post-intervention follow-up = 5.4 weeks), equivalent to an increase of 0.34 cups of fruit and vegetables a day. It is uncertain whether there are any short-term differences in child consumption of fruit and vegetables in meta-analyses of trials examining parent nutrition education versus no intervention (SMD 0.13, 95% CI -0.02 to 0.28; 11 trials, 3050 participants; very low-quality evidence; mean post-intervention follow-up = 13.2 weeks). We were unable to pool child nutrition education interventions in meta-analysis; both trials reported a positive intervention effect on child consumption of fruit and vegetables (low-quality evidence). Very few trials reported long-term effectiveness (6 trials), cost effectiveness (1 trial) or unintended adverse consequences of interventions (2 trials), limiting our ability to assess these outcomes. Trials reported receiving governmental or charitable funds, except for four trials reporting industry funding. Despite identifying 80 eligible trials of various intervention approaches, the evidence for how to increase children's fruit and vegetable consumption remains limited in terms of quality of evidence and magnitude of effect. Of the types of interventions identified, there was moderate-quality evidence that multicomponent interventions probably lead to, and low-quality evidence that child-feeding practice may lead to, only small increases in fruit and vegetable consumption in children aged five years and under. It is uncertain whether parent nutrition education or child nutrition education interventions alone are effective in increasing fruit and vegetable consumption in children aged five years and under. Our confidence in effect estimates for all intervention approaches, with the exception of multicomponent interventions, is limited on the basis of the very low to low-quality evidence. Long-term follow-up of at least 12 months is required and future research should adopt more rigorous methods to advance the field. This is a living systematic review. Living systematic reviews offer a new approach to review updating, in which the review is continually updated, incorporating relevant new evidence as it becomes available. Please refer to the Cochrane Database of Systematic Reviews for the current status of this review.

Hodder RK ，O'Brien KM ，Tzelepis F ，Wyse RJ ，Wolfenden L ... -  《Cochrane Database of Systematic Reviews》