systematic review nutrition education

School-Based Nutrition Interventions in Children Aged 6 to 18 Years: An Umbrella Review of Systematic Reviews

Affiliations.

• 1 School of Medicine and Public Health, University of Newcastle, University Drive, Callaghan, Newcastle, NSW 2308, Australia.
• 2 Priority Research Centre in Health and Behaviour, University of Newcastle, University Drive, Callaghan, Newcastle, NSW 2308, Australia.
• 3 Hunter New England Population Health, Longworth Avenue Wallsend, Newcastle, NSW 2287, Australia.
• 4 Hunter Medical Research Institute, Kookaburra Circuit, New Lambton Heights, Newcastle, NSW 2305, Australia.
• 5 National Centre of Implementation Science, University of Newcastle, University Drive, Callaghan, Newcastle, NSW 2308, Australia.
• 6 Faculty of Health, Arts and Design, Swinburne University of Technology, John Street, Hawthorn, VIC 3122, Australia.
• PMID: 34836368
• PMCID: PMC8618558
• DOI: 10.3390/nu13114113

Schools are identified as a key setting to influence children's and adolescents' healthy eating. This umbrella review synthesised evidence from systematic reviews of school-based nutrition interventions designed to improve dietary intake outcomes in children aged 6 to 18 years. We undertook a systematic search of six electronic databases and grey literature to identify relevant reviews of randomized controlled trials. The review findings were categorised for synthesis by intervention type according to the World Health Organisation Health Promoting Schools (HPS) framework domains: nutrition education; food environment; all three HPS framework domains; or other (not aligned to HPS framework domain). Thirteen systematic reviews were included. Overall, the findings suggest that school-based nutrition interventions, including nutrition education, food environment, those based on all three domains of the HPS framework, and eHealth interventions, can have a positive effect on some dietary outcomes, including fruit, fruit and vegetables combined, and fat intake. These results should be interpreted with caution, however, as the quality of the reviews was poor. Though these results support continued public health investment in school-based nutrition interventions to improve child dietary intake, the limitations of this umbrella review also highlight the need for a comprehensive and high quality systematic review of primary studies.

Keywords: adolescent; child; dietary intake; intervention; nutrition; school-based; umbrella review.

Publication types

• Adolescent Behavior*
• Child Behavior*
• Environment
• Feeding Behavior*
• Health Behavior*
• Health Education
• Health Promotion / methods*
• School Health Services*
• Systematic Reviews as Topic
• Telemedicine

Grants and funding

• APP1153479/National Health and Medical Research Council
• APP1160419/RKH NHMRC Early Career Fellowship

• Open access
• Published: 11 July 2017

Systematic review of control groups in nutrition education intervention research

• Carol Byrd-Bredbenner 1 ,
• FanFan Wu 1 ,
• Kim Spaccarotella 2 ,
• Virginia Quick   ORCID: orcid.org/0000-0002-4338-963X 1 ,
• Jennifer Martin-Biggers 1 &
• Yingting Zhang 1  

International Journal of Behavioral Nutrition and Physical Activity volume  14 , Article number:  91 ( 2017 ) Cite this article

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Well-designed research trials are critical for determining the efficacy and effectiveness of nutrition education interventions. To determine whether behavioral and/or cognition changes can be attributed to an intervention, the experimental design must include a control or comparison condition against which outcomes from the experimental group can be compared. Despite the impact different types of control groups can have on study outcomes, the treatment provided to participants in the control condition has received limited attention in the literature.

A systematic review of control groups in nutrition education interventions was conducted to better understand how control conditions are described in peer-reviewed journal articles compared with experimental conditions. To be included in the systematic review, articles had to be indexed in CINAHL, PubMed, PsycINFO, WoS, and/or ERIC and report primary research findings of controlled nutrition education intervention trials conducted in the United States with free-living consumer populations and published in English between January 2005 and December 2015. Key elements extracted during data collection included treatment provided to the experimental and control groups (e.g., overall intervention content, tailoring methods, delivery mode, format, duration, setting, and session descriptions, and procedures for standardizing, fidelity of implementation, and blinding); rationale for control group type selected; sample size and attrition; and theoretical foundation.

The search yielded 43 publications; about one-third of these had an inactive control condition, which is considered a weak study design. Nearly two-thirds of reviewed studies had an active control condition considered a stronger research design; however, many failed to report one or more key elements of the intervention, especially for the control condition. None of the experimental and control group treatments were sufficiently detailed to permit replication of the nutrition education interventions studied.

Conclusions

Findings advocate for improved intervention study design and more complete reporting of nutrition education interventions.

A major goal of nutrition education research is to elucidate factors that enable individuals to improve diet-related behaviors and/or cognitions associated with better health and greater longevity. These factors can then be incorporated in educational and health promotion interventions which, in turn, can be evaluated to determine whether the intervention effects change behaviors and/or cognitions among those assigned to the intervention vs. those in a control condition.

Well-designed research trials are critical for determining the efficacy and effectiveness of new interventions [ 1 ]. The basic components of educational research intervention trials include experimental variables, such as a novel curriculum; strong, measurable research questions or hypotheses; valid and reliable instruments for documenting change in behavior and/or cognitions; a strong data analysis plan; and an experimental design that minimizes threats to internal validity. To determine whether behavioral and/or cognition changes can be attributed to the intervention, the experimental design must include a control or comparison condition against which outcomes from the experimental group can be compared [ 2 , 3 , 4 , 5 ]. The randomized controlled trial (RCT) is typically considered the “gold standard” for ascertaining intervention efficacy and effectiveness [ 2 ].

Experts emphasize that to robustly minimize biases and variability of factors that may influence intervention trial outcomes, the control and experimental conditions must: 1) contain randomly assigned participants; 2) occur simultaneously to ensure both conditions experience the same history (i.e., external events, such as political change, natural disasters, scientific discoveries) and maturation (i.e., internal events, such as physical growth, memory decline with aging); 3) be structurally equivalent on as many non-specific factors as possible (i.e., factors other than the “active” ingredients in the experimental condition, such as participant time commitment, format and timeline of activities and data collection, and extent of attention and support from research staff) [ 5 ]; and 4) offer equal value, attractiveness, credibility, and outcome expectations to keep participants blind to their condition assignment and thereby avoid novelty effects, differential dropout rates, disappointment arising from assignment to the control group, and/or efforts by control group participants to seek an alternate source of the treatment offered to the experimental group [ 1 , 3 , 4 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. The control condition also must not modify the intervention’s specific factors (i.e., behavior and/or cognitions targeted in the experimental condition) [ 4 , 7 ].

To reduce the risk of a Type 1 error (acceptance of an ineffective intervention) [ 1 , 9 , 17 ], treatment received by control condition participants should differ from those in the experimental condition only in the non-receipt of the “active ingredient” of the intervention hypothesized to affect study outcomes [ 4 , 6 ]. Rigorous control of non-specific factors, however, tends to increase intervention research costs because a plausible control intervention must be developed and implemented. Additionally, as the stringency of control exerted over non-specific factors increases, the risk of understating the effectiveness of the intervention rises because effect size is inversely associated with rigor of non-specific factor control [ 9 , 17 , 18 , 19 ]. Therefore, to demonstrate statistically meaningful differences, larger sample sizes are needed to avoid Type 2 errors (failure to recognize an intervention is effective) and detect treatment effects when the control and experimental group treatments are structurally equivalent than when a less equivalent control treatment is used [ 1 , 9 , 17 ].

A key challenge to nutrition education researchers is selecting a suitable treatment for the control condition that is congruent with the research question, study resources, availability of standard treatment/usual care, and ethical considerations [ 7 , 9 , 10 , 12 , 20 , 21 ]. Control condition participants may receive treatment ranging from nothing at all to extensive treatment in an alternate “active” control condition unrelated to the experimental condition. As indicated in Table 1 , the type of control condition selected can have important effects on study resources, participants, internal validity, and outcomes. For instance, resource investment in the treatment for the control condition can range from zero for the inactive control to considerable for active control. Ethical issues may be more highly problematic in inactive control conditions when participants in need of the intervention are denied treatment, but ethical issues are lessened when a standard or usual treatment can be offered. Preventing disappointed control group participants from seeking alternate sources of the treatment may not be possible, which weakens internal validity and undermines a true evaluation of the intervention’s effect. Even in active control conditions where participants receive a contemporaneous intervention equal to the treatment condition in all aspects, except the “active ingredient”, researchers may inadvertently treat control participants differently. Those delivering the intervention (e.g., research staff, educators) also may dislike being in the control condition [ 22 ] and seek opportunities to provide participants with treatment like that being given to the experimental group.

Clearly, the efficacy and “effectiveness of the experimental condition inherently depends as much on the control condition as on the experimental condition” [ 1 ],p.276. Despite the impact different types of control groups can have on study outcomes [ 23 ], the treatment provided to participants in the control condition has received limited attention in the literature [ 1 , 7 , 12 , 17 , 20 , 24 , 25 , 26 ] and sometimes is not even described in research designs [ 27 , 28 ]; yet in the words of Mohr et al. with regard to psychological interventions, “inappropriate control conditions can overestimate the effectiveness of a treatment, or kill off a potentially useful treatment” [ 1 ],p.283. Thus, a systematic review of control groups in nutrition education interventions was conducted with the goal of better understanding how control conditions are described in peer-reviewed primary outcomes journal articles in comparison with experimental conditions. An additional goal of this investigation is to open discussions among colleagues as to how best to improve reporting of control and experimental condition treatments in intervention evaluation studies to facilitate advancement of the field.

A systematic literature search was conducted after review of guidance from the Nutrition Education Systematic Review Project [ 29 ]. The study team then identified databases to use in the systematic review, search terms, and inclusion and exclusion criteria.

Search strategies

Search strategies were formulated according to the PRISMA guidelines [ 30 ]. Subject headings or search terms unique to each database were identified and searched in combination with keywords derived from the major concepts of “nutrition education intervention” and “control groups” or “study design”. Table 2 shows the final search strategy for the selected databases (i.e., CINAHL, PubMed, PsycINFO, WoS, and ERIC). Searches were conducted in winter 2016.

To be included in the systematic review, the articles had to report primary research findings of controlled nutrition education intervention trials from peer-reviewed journals. Included studies could address content other than nutrition, but nutrition had to be a key component. Additionally, included interventions had to focus on health promotion and disease prevention and have an education component. Inclusion criteria also required that interventions consist of more than one session and be conducted in the United States with free-living consumer populations. All included articles were published in English between January 2005 and December 2015. In cases where more than one article from the same study was located, only primary outcomes paper was included in the review to prevent over-representation of the type of control group used.

Excluded articles were studies reporting pilot, feasibility, cross-sectional, follow-up, or secondary analysis findings and those lacking a control or comparison group. Studies that focused on weight loss or disease management/treatment and those lacking an education component (e.g., those solely manipulating environmental factors) also were excluded. Additionally, all studies targeting professionals (e.g., health care, child care) or individuals recruited due to a pre-existing disease, such as diabetes, eating disorders, and obesity, or hospitalization, were excluded.

Data management

Citations for the 1164 articles returned by the systematic literature search were entered in a citation management tool (Fig. 1 ). After removal of duplicates ( n  = 46) and publications that were not complete primary research articles (e.g., commentaries, viewpoints, editorials, letters, survey studies, abstracts, review articles, n  = 50), two members of the study team independently conducted an initial screening of all article titles to identify those congruent with the study purpose. The title review yielded 195 articles that appeared to meet inclusion criteria. Next, article abstracts were independently reviewed by the same team members and 83 were identified as congruent with study purposes. Four team members scanned the articles and identified 53 articles meeting inclusion criteria. During data extraction, 10 additional articles were eliminated because they did not meet inclusion criteria thereby yielding a total of 43 reviewed articles.

Flow chart of literature search results for controlled research studies reporting (e.g., not secondary analysis or pilot, feasibility, or follow-up studies) results of nutrition education primary-prevention (e.g., not part of treatment for disease or weight loss) interventions consisting of more than one session conducted with free-living individuals in the United States

Data collection and analysis

After scrutinizing guidance from the Nutrition Education Systematic Review Project [ 29 ] and Cochrane Collaboration [ 31 , 32 ] as well as previously published systematic reviews [ 33 , 34 , 35 ], data extraction tables were designed by the study team. These tables were iteratively pilot-tested and refined.

Data were extracted by one team member and independently checked for accuracy by two other team members. As shown in Table 3 , the factors extracted included treatment provided to the experimental and control groups, overall intervention content, procedures used to tailor the intervention to participants, intervention delivery mode (e.g., group, individual), intervention format (e.g., curriculum, website, brochure) and duration, intervention setting, individual intervention session description (e.g., number of sessions or interactions, session duration, session frequency, content of each session, time allotment for each session component, overall duration of the intervention), procedures for standardizing intervention across multiple sites/practitioners, procedures for assessing fidelity of implementation across multiple sites, and procedures for blinding (masking) participants and/or intervention staff to participant group assignment, rationale for control group type selected, as well as sample size, attrition rate, and theoretical foundation. The goal of the factors extracted was to document the explicit presence or absence of each factor reported in the article. Additionally, only the 43 articles identified in the search were reviewed; extracting additional data from bibliographical references to previous developmental work cited in articles was beyond the scope of this study. A written narrative describing the treatment groups was prepared for each study. Extraction tables were content analyzed by team members to identify themes used to prepare a narrative synthesis of findings.

The treatment provided to the experimental and control conditions in the studies meeting the inclusion criteria are described in Table 4 . For accuracy, these descriptions used verbiage from the original research inasmuch as possible [ 36 ]. More than one-third of the 43 studies in the review had an inactive control condition; that is, the control group received no treatment or delayed-treatment (or wait-list). Because a key goal of this study was to compare how control and experimental conditions are described in peer-reviewed literature, results will focus on the 28 studies that had an active control condition. Of these studies, 7 had a usual or standard treatment for the control group, 12 offered an alternative active treatment to control participants, and 9 were dismantling (or additive) component active controls (2 of the 9 were mixed in that control groups received an alternative active treatment whereas the experimental groups received additive treatments).

Factors extracted in reviewed articles

Additional file 1 Table S5 compares the presence of factors extracted in the systematic review of articles. Each factor is described below, citing examples of studies demonstrating the factor

Description of overall intervention content

Reviewed articles commonly included a description of the overall intervention content provided. Content tended to focus on increasing fruit and/or vegetable intake, lowering fat intake, and healthy eating in general. The extensiveness of the overall content description for experimental groups ranged from only naming the general topic area (e.g., fruits and vegetables) [ 37 ] to listing topics and content addressed [ 38 , 39 ] to reporting content and participant activities [ 40 , 41 , 42 ] and teaching strategies [ 43 , 44 , 45 , 46 ].

Descriptions of the overall content for the control conditions tended to provide much less detail compared to experimental conditions. For example, among those employing usual or standard treatment, one study indicated only that “control classrooms did not receive vegetable-related instruction” [ 40 ],p.39 whereas another study reported that health education with no nutrition content was given [ 43 ], with neither indicating what control group participants received. Other descriptions of the control condition of usual treatment studies were equally vague indicating these participants received “traditional”, “regular”, or “normal” lessons [ 37 , 38 , 41 , 47 ]. Descriptions of treatment provided to the control groups in some alternative active treatment studies also were vague (e.g., control received pamphlet on fruits and vegetables [ 48 ], “packet of 5 printed commercially available booklets [ 49 ],” videos on sleep disorders [ 50 ]). However, several alternative active treatment investigations were more informative, including content similar in detail to the experimental group [ 46 , 49 , 51 , 52 , 53 ]. Dismantling studies tended to provide the greatest detail about the control condition largely because most experimental conditions were additive to the base formed by the control.

Description of how the intervention was tailored

Unless a goal of an investigation was to determine the effects of tailoring, little information on this factor was reported for experimental or control conditions regardless of whether a usual or other active control condition was used. In usual treatment control conditions, only one study mentioned tailoring for the experimental group [ 37 ]. A few alternative active treatment control condition studies tailored experimental and control treatments to demographic characteristics (e.g., older adult learners, African American women) [ 51 , 52 ]. Some investigations tailored treatments for experimental groups by allowing participants to choose topics or materials [ 45 , 49 ], with one study giving both experimental and control groups the ability to select topics [ 51 ]. The aim of most dismantling studies was to assess the effects of tailoring (experimental groups) vs not tailoring (control group); thus, tailoring descriptions for the control group generally were not applicable. On the other hand, the relative importance of the tailoring method to study aims made reasonably complete descriptions of this process requisite to report for experimental groups. Gans et al. reported [ 54 ] that tailoring was based on participant’s fat, fruit, and vegetable intake and related behaviors, self-identified needed behavior changes, personal motivators, barriers, and other psychosocial issues associated with healthy eating, needs, and interests. Resnicow et al.’s [ 53 ] report is notable in that these authors provided a table describing messages and graphic images used to tailor study newsletters.

Description of intervention delivery mode, material type used, duration, and setting

Across all types of control conditions, investigators consistently reported the intervention delivery mode, with the most common being group sessions or online. Descriptions for experimental conditions tended to express delivery mode in explicit terms whereas for control conditions, it was often left to the reader to decide on the mode using implicit clues. This was particularly the case when the control group received a “usual” treatment without further clarification [ 40 , 41 , 43 , 47 , 55 ].

The type of material that provided intervention content directed to participants tended to be printed (e.g., brochures, pamphlets, manuals, newsletters) and online (e.g., websites, videos). Interventions delivered by instructors to groups used mostly curricula and “lessons.” Some of the reviewed articles gave bibliographical references, internet links, or other means for obtaining intervention materials, with sources for instructional materials more commonly given for experimental than control groups [ 38 , 40 , 41 , 42 , 43 , 47 , 55 , 56 , 57 , 58 , 59 ]. An examination by control group type found that references for resources used to deliver usual treatment to control groups were not included. Among alternative active treatment studies, the material types used with both experimental and control groups had comparably detailed descriptions [ 39 , 42 , 51 , 60 ], with some exceptions where great detail about the materials used by the experimental group was provided while giving only limited descriptions of those intended for the control group [ 44 , 48 ]. Material type descriptions tended to be more even across dismantling studies.

Total duration of the intervention delivered to the experimental group was explicitly stated in nearly all studies reviewed. For control groups, total duration was less likely to be clearly described and frequently had to be deduced from a review of the study timeline (e.g., when the baseline and post-test was administered) and comparison to statements made about the experimental group. The setting where group sessions were delivered normally was overtly indicated (e.g., school, community center). Interventions directed to individuals who received mailed materials or used websites generally only implied the setting as being home or worksite [ 49 , 50 , 56 , 57 ] and did not report where participants generally used intervention materials.

Description of individual intervention sessions

Across all types of control groups, the number of sessions or interactions (e.g., newsletters) usually was explicitly stated for both treatment groups. The duration of individual sessions or length of materials was more commonly reported for experimental than usual treatment control groups; for other types of control groups, duration was somewhat more consistently reported for both treatment groups [ 48 , 61 ]. Reporting of frequency of sessions was fairly even across experimental and control groups in all types of control conditions except usual treatment, where this information was rarely included.

Reports of the content of individual sessions/interactions were provided in about half the active control articles reviewed with most descriptions being abbreviated for the experimental group and virtually non-existent for the control group. In a few cases, researchers provided a table or figure listing concepts/topics/objectives addressed in each session/interaction for the experimental group [ 40 , 41 , 54 , 61 , 62 ]. Only 2 studies provided a table describing the content of both the experimental and control treatments [ 46 , 49 ]. Descriptions of the duration of each main component of individual sessions/interactions were rare. The exceptions were Ratcliffe et al. [ 61 ] who stated “[e]ach hour-long session consisted of approximately 20 min of instruction followed by 40 min of hands-on garden experiences”p.38, Herbert et al. [ 38 ] who reported “Energize engages children in 1, 60-minute class once a week … by involving them in 15 minutes of nutrition education, a 10-minute warm-up … and 35 minutes of aerobic exercise activities and fitness games”p.781, and Pobocik et al. [ 41 ] who indicated “[a]pproximately 20 minutes of the 45-minute class were allotted to presenting information … remaining time … for testing, activities, and demonstrations”p.22. Comparable descriptions for control groups were not included.

Procedures for standardization across centers/practitioners

Procedures for standardizing the experimental condition intervention delivery across centers/practitioners took several forms, including training instructors [ 38 , 40 , 43 , 45 , 47 , 52 , 55 ] and utilizing pre-established curricula (instructional lessons and protocols) [ 38 , 40 , 41 , 43 , 47 , 55 ] and/or instructional materials (e.g., printed materials, videos, websites) [ 37 , 48 , 49 , 50 , 56 , 57 ]. Standardization procedures were similarly addressed across types of interventions for the experimental group. In contrast, little information related to standardization of implementation of control group treatments was provided for usual treatment control conditions. In alternative and dismantling active treatment studies, the procedures for standardizing control group treatment were frequently addressed and mostly took the form of pre-established instructional materials [ 39 , 49 , 50 , 52 , 53 , 54 , 56 , 57 , 59 , 63 , 64 , 65 ].

Procedures for assessing fidelity of implementation

Only about half of active control studies addressed fidelity of adherence to procedures, with most of these including information about procedures for both the experimental and control conditions. Methods used to establish fidelity of implementation for both experimental and control groups in active control studies where teachers or instructors delivered the treatment included detailed/scripted presentations [ 43 , 46 ], frequent meetings with researchers [ 38 , 46 , 47 ], random observation/videotaping of instructors [ 43 , 46 , 55 ], teaching/feedback logs [ 43 , 52 ], and audiotaping [ 57 ]. Methods used in active control group studies in which participants self-directed their engagement with pre-established treatments (e.g., web-based, printed materials) included completing forms documenting usage of treatment materials immediately after use [ 50 , 64 , 65 ], self-report posttest survey items that gauged extent of treatment use [ 53 , 58 ], and website tracking data [ 59 ].

The vast majority of active control studies provided little detail about fidelity procedures. One notable exception was McCaughtry et al. [ 43 ], who described fidelity procedures as including “very detailed (nearly scripted) lessons in the curriculum…a research assistant [who] conducted randomized school visits to observe each health education teacher’s instruction to guarantee that the control teachers were not teaching nutrition content and that the intervention teachers were implementing the curriculum with fidelity,”p.279. Another noteworthy example was provided by Wolf et al.: “Treatment fidelity checks were conducted on 200 (41%) of the intervention calls. Trained raters listened to audio recordings of the calls and completed a checklist documenting whether specific points were covered and whether the interventionist spoke at an appropriate pace, responded to questions with clear answers and probed at appropriate times” [ 57 ],p.34.

Procedures for blinding participants and researchers to treatment group assignment

Limited attention was given to the issue of blinding participants or researchers in the reviewed articles. In many cases, it was not clear whether participants were blinded (or aware there was a control vs experimental group), although this is a typical component of informed consent procedures. None of the studies providing the control group with usual treatment addressed participant blinding. Two articles blinded participants to group assignment by explaining that they were getting one of two programs or using alternate names for “control” or “experimental” groups. In specific, McCarthy et al. stated “A portion of the script used by project staff read … This is a cancer prevention study to compare two programs designed to help black women reduce their risk of cancer and improve their appearance. The first program involves 8 weekly 2-h sessions on diet and exercise. The second program involves 8 weekly 2-h sessions on current health topics of interest to black women, such as breast cancer and menopause. Both programs will be conducted by black women physicians and other professionals. We'll decide which group you'll be assigned to randomly, for example, by flipping a coin…” [ 51 ], p.247. In McClelland et al.’s crossover design study, these researchers assigned participants “to either the Apples Group (n=6) with the treatment curriculum … delivered first or the Beans Group (n=7) with the control curriculum … delivered first” [ 42 ], p.2. Another study reported that participant blinding efforts may not have worked. These researchers stated that “[g]irls, mothers, and troop leaders were masked to their group membership assignment;” but went on to say “because the project was called the Osteoporosis Prevention Project, some individuals in the control troops may have determined their status owing to the generic health focus of the sessions” [ 46 ],p.158.

The issue of blinding research staff likely is less important when interventions are automated and participant exposure to staff is minimal or non-existent. However, even when there was significant interaction with staff (e.g., in interventions delivering in-person or phone-based treatments), studies rarely addressed staff blinding. A few investigators reported using different instructors for experimental and control conditions [ 51 , 52 ], whereas others indicated that instructors were not blind to condition due to the nature of the intervention [ 46 , 55 , 57 ]. Blinding also would have been difficult in some of the dismantling studies where part of the treatment for only one of multiple experimental groups involved live interactions with staff [ 59 , 63 ]. In a few cases, articles reported that study evaluators were kept blind to participant study group assignment [ 57 , 58 , 64 ].

Rationale for selection of control group type

Reviewed studies seldom provided a rationale for the type of control group used and for those that did, various reasons were cited. These included convenience and comparability (e.g., “Three comparison [college] courses … were selected because they also were upper-level Human Biology courses, were delivered the same quarter, and were taught by experienced health promotion researchers and focused on a health message” [ 44 ], p.544) and relative strength (e.g., “Control group participants received fewer follow-up mailings … [that] resulted in a difference in “attention” between treatment arms, it is nonetheless a stronger design than a no-treatment control group” [ 60 ], p.62). Appropriateness to setting and participants also was considered (e.g., “Employees … were … assigned to the Web-based … or the print condition. It was recognized that the print materials could also be effective instruments of health behavior improvement (unlike a no-treatment control group) and could be a challenge as a control group … [and] would be a likely workplace alternative to an online program; therefore, the print group was thought to be an appropriate control group for the study” [ 49 ], p.e17). Yet, after finding both interventions yielded similar improvements, the article added to the control group rationale by stating… “[b]ecause it was originally thought that the print materials would form a relatively weak intervention compared to the Web program, a no-treatment control was not included in the design” [ 49 ],p.e17. Only 3 studies indicated the rationale for the control group was to control for non-specific effects (i.e.,“[t]he control group provided an intervention of identical intensity and program delivery format as the experimental group, ruling out “attention” effects in the experimental group” [ 52 ],p.386, “we used an attention control group to take into account the effect of participation” [ 65 ],p.37, and “[t]he purpose of this group was to control for any nonspecific effects from being educated about healthy lifestyles and from contact time and number of sessions … with professionals [ 46 ],p.158.”

Behavior change theory use

Nearly one-quarter of all reviewed studies did not indicate whether a theory was used to guide the intervention. Of those that indicated application of a behavior change theory, more than half used the social cognitive theory and about one-quarter used the transtheoretical model. Most studies named the theory used with little additional explanation of how it was operationalized. The most explicit reporting of theory application was by Pobocik et al. [ 41 ], who included a table listing social cognitive theory constructs, definition of the construct, and an example of how the construct was operationalized in the Do Dairy intervention. Of those reporting how theories were applied, several used the stage of change construct for tailoring materials [ 48 , 63 , 66 ] and/or selecting assessment scales [ 40 , 48 , 50 , 54 , 64 ]. Particularly illustrative of theory use in assessment were the tables Wall et al. [ 40 ] and Elder et al. [ 64 ] provided that listed theory constructs and corresponding evaluation items.

Comparison across control condition types

In the 7 investigations using a usual or standard active control condition, consisting of “traditional” or “regular” instruction, participants tended to be children enrolled in school or participants in government sponsored programs—perhaps because these systems have an ongoing program available for comparison. Articles gave fairly complete descriptions of the intervention provided to the experimental group, which were mostly curriculum based. They tended not to indicate if or how interventions were tailored and rarely provided information on the content of each session/interaction or how time was apportioned in each session, although this information may be available in the curricula referenced. With regard to the control group intervention, other than the overall intervention content, delivery (individual or group), and setting, little other information was provided. In most cases, too little information was provided about the usual treatment to determine whether the control group’s treatment was comparable on non-specific factors to that received by the experimental group [ 38 , 40 , 41 , 47 , 55 ]. Descriptions in one study, which compared differences in teaching strategies (e.g., traditional vs. tailored online) indicated fairly similar attention to non-specific factors [ 37 ].

In the 12 studies providing an alternative active treatment to the control group, investigators included a fairly even description of the treatments given to both experimental and control groups—a notable exception for both groups was a lack of specificity regarding the amount of time in each session devoted to the main components of the treatment. Additionally, many of the interventions were mail- or web-based and did not explicitly indicate the intervention setting. A comparison of the intensity of the treatments offered indicates that in some studies, the control group received “lighter” treatment doses than the experimental group (e.g., control group received a single pamphlet whereas the experimental group received tailored monthly magazines for 8 months [ 48 ], packet of printed booklets vs. highly interactive web-based program [ 49 ], manual vs manual coupled with coaching calls, tailored newsletters, and personalized feedback [ 56 ]). Many studies appeared comparable across a range of non-specific factors that could affect study outcomes [ 42 , 51 , 52 ]. One example of comparable treatment is Wolf et al. [ 57 ] who provided both experimental and control groups with a brochure (different topics) and tailored telephone education. Healy et al. [ 39 ] offers a second example in which both groups received a treatment that was the same length of time (7 50-min sessions over 1.5 weeks), used similar teaching strategies (i.e., lecture, discussion, question/answer, group activities), and differed only on content taught.

The 7 dismantling (additive) component active control studies tended to have 2 or more experimental groups. Interestingly, in all but one of these studies, the differences between the experimental and control treatments hinged on tailoring [ 61 ]. The control, or comparison, group in nearly all of these studies received less personalized and less intensive treatment than the experimental group [ 54 , 59 , 61 , 63 , 64 ]. In one study, for example, 3 groups of women either received non-tailored newsletters, tailored newsletters, or tailored newsletters and visits with lay health advisors [ 64 ]. Because of the derivative nature and increasing intensity of treatment provided by most dismantling studies [ 54 , 59 , 61 , 63 , 64 ], there was an imbalance in non-specific factors between/among study groups. The in-person and frequent phone contact received by one experimental group vs ongoing access to the project website and automated individual risk profiling given to a second experimental group vs printed materials provided one time to control participants demonstrated the imbalanced attention across study groups [ 59 ]. Among dismantling studies, the greatest balance in non-specific effects was achieved by Resnicow et al. [ 53 ] in that both experimental and control groups received the same newsletters except the tailoring of the experimental newsletters was more specific.

An additional two dismantling studies were classified as “mixed” [ 46 , 65 ] because the control participants received an alternate treatment that was not a derivative of the experimental group but was similar to treatment provided to control participants in alternative active treatment conditions. For instance, control condition participants in one study received 2 45-min web sessions on anatomy whereas those in the 2 experimental groups received 2 45-min web sessions on nutrition or 2 45-min web sessions plus a 45-min booster session [ 65 ]. The comparability of treatment provided to control groups in these 2 mixed dismantling active control studies tended to be more balanced on non-specific factors than the other 7 dismantling studies that did not have an alternative treatment.

Other findings

Reports of sample size and attrition were uneven. Some studies provided a complete description of total numbers recruited and retained, by treatment group, at each phase of the study [ 55 , 67 ], with several including CONSORT diagrams [ 48 , 53 , 54 , 55 , 57 , 58 , 59 , 63 , 65 , 66 , 68 , 69 ]. However, other studies only reported sample sizes at baseline [ 70 ], posttest [ 43 , 71 , 72 ], those completing both pretest and posttest [ 22 , 45 ], or sample sizes and/or attrition rates for both groups combined [ 41 , 73 ].

More than 3 out of 4 studies reviewed had random assignment of participants or intact groups (e.g., classrooms). Of the 10 non-randomized trials, half had no treatment control conditions. Of the remainder, one did not address randomization [ 41 ], one indicated the experimental group was comprised of students in classrooms with teachers who volunteered to participate [ 38 ], and another involving college students used intact classes and did not randomize the classes [ 44 ]. Two studies offered more explanation. One that was offered in WIC clinics indicated randomization was impractical and stated that “the practicality of being able to actually study comparisons of nutrition education intervention modalities in a typical clinic setting overcompensated for the lack in ability to develop a randomized design” [ 37 ],p.754. Authors of the second study offered this rationale, “The high cost and limited availability of randomized controlled trials in community settings highlight a need to evaluate and report on nonrandomized interventions that can be implemented in existing community settings” [ 45 ],p. 265.

Terminology used to describe control groups was not always consistent with definitions in Table 1 . For example, two papers referred to control groups who received usual instruction as no treatment controls [ 37 , 43 ]. Another provided an alternative active treatment, yet referred to it as a standard treatment [ 48 ]. Still another referred to the alternative active treatment control group as an attention placebo group [ 65 ]. A placebo should have no effect on a person, however because learning likely occurred in this and other alternate education-related control conditions, the term placebo does not accurately describe the control condition.

The goal of this study was to conduct a systematic review of control groups in nutrition education interventions and describe how control conditions are reported in peer-reviewed primary outcomes journal articles in comparison with experimental conditions. The findings of this systematic review indicate that the articles sampled focused on a wide array of controlled nutrition education intervention studies. Most addressed fruits and vegetables, fat intake, and healthy eating and tended to target school children as well as limited resource youth and families enrolled in government sponsored programs. Overall, descriptions of experimental conditions, regardless of type of active control condition, tended to be far more complete than descriptions of control conditions. Studies tended to report nearly all key factors (i.e., intervention content, delivery mode, material type, total duration, setting, individual session/interaction components [e.g.,, number, duration or length, frequency, content], standardization procedures, procedures for assessing fidelity of implementation, references for materials, theoretical underpinnings, and randomization) for the experimental condition. However, descriptions of the experimental group commonly lacked procedures for blinding and tailoring (except when the study was comparing differences in the effect of tailoring). In contrast, control conditions lacked descriptions of many key factors, with the most commonly omitted factors being individual sessions/interactions (e.g., number, duration, frequency, content of individual sessions), procedures for standardization, procedures for assessing implementation fidelity, blinding procedures, rationale for the type of control group selected, and references for instructional materials. Additionally, the factors that were reported for control conditions tended to be less explicit and included fewer details than provided for the experimental condition. In many cases, too little information was provided to determine the comparability of the control group vis-à-vis non-specific factors. Overall, the descriptions of both control and experimental group treatments became more complete as the type of active control became stronger and more complex; that is, alternative active treatments and dismantling studies provided the most detailed descriptions of the control group condition whereas usual or standard control conditions provided the least detail.

One-third of the 43 reviewed studies had inactive control conditions (i.e., no treatment or delayed treatment), a research design that is considered weak [ 7 , 17 ]. The Food and Drug Administration instructs that a no-treatment control be used only when investigation outcomes are entirely objective and cannot be biased by lack of blinding [ 74 ]—although this advice is directed at drug trials, it can be reasonably applied to education trials using inactive controls. For instance, in one delayed treatment study, researchers stated that a lack of blinding among those teaching the educational intervention was problematic (i.e., they “generally did not like to be randomized to the control condition [ 22 ],p.31”). Failure to implement procedures to prevent differential treatment, commitment, and engagement of both experimental and control condition instructors has the potential to confound results [ 75 ]. Likely many researchers conducting the 43 reviewed studies had implemented appropriate blinding procedures for participants, instructors, and researchers; however descriptions of procedures for blinding and/or prevention of differential treatment were not reported in most studies.

Active control conditions, considered a stronger research design than inactive [ 7 ], were used in two-thirds of the reviewed studies. In this and other studies [ 76 ], usual treatment was considered an active control whereas some researchers categorize usual treatment as inactive (or passive) because it typically is not structurally equivalent on non-specific factors to the experimental condition [ 6 , 7 , 24 , 32 , 76 ]. All usual treatment conditions in reviewed studies offered control groups traditional or regular instruction that did not include content offered to the experimental group. As Street and Luoma point out, it usually is not possible to equalize all non-specific factors (particularly credibility and outcome expectations) when using education about an unrelated topic as the usual treatment [ 6 ]. The limited information about the usual treatment given to control participants negated the possibility of confidently affirming equivalency of intensity and structure of control and experimental treatments.

A hallmark of evidence building is replicability. Similar to findings by researchers in other fields [ 12 , 26 ], none of the experimental and control group treatments were sufficiently detailed to permit replication of the nutrition education interventions studied. About half of the experimental treatment descriptions included a reference for the intervention materials and a third of the control treatment descriptions included this information; these materials may mitigate replication issues associated with missing information in the reviewed article. Another alternative is to contact authors to obtain intervention details. When Glasziou et al. contacted authors who published non-pharmacological medical treatment intervention outcomes, treatment descriptions improved significantly; however one-third of the studies they reviewed still had insufficient detail, in part because study authors did not respond despite repeated attempts or were unwilling to provide additional information [ 26 ].

Standardization and fidelity procedures are equally important for control and experimental conditions—without these procedures, either group may receive more or less than the research protocol intended which likely will confound outcomes [ 75 , 77 , 78 ]. The limited reporting of standardization procedures (e.g., use of manuals, standard operating procedures) and process evaluation activities in the reviewed studies, and noted by others in psychological therapy research [ 77 ], indicates that either reports are incomplete or these procedures were not implemented—neither of which are helpful when trying to weigh the value of the study outcomes and determine whether treatment groups received differential treatment from unblinded research staff.

Random assignment is considered critical to minimizing biases in trial outcomes and maximizing accuracy of analysis of intervention effects. One-quarter of the reviewed studies did not randomly assign participants, and likely suffered from at least some selection bias [ 79 ]. Compounding the lack of randomization is that many of these same studies did not address participant or researcher blinding and/or procedures for assessing intervention implementation fidelity, all of which impair internal validity [ 79 ].

Reporting sample size seems like a fairly straightforward task, regardless of how complex an intervention design may be. Indeed, CONSORT flow diagrams [ 80 ] make reporting changes in sample size at each stage of the study clear and easy to report. Yet, many of the studies reviewed lacked key sample size information, a phenomenon noted by others [ 81 , 82 ]. In some cases, sample size was not declared in tables reporting data [ 38 , 47 , 51 , 72 ].

It is interesting that so few articles provided a justification for the type of control condition used, especially given this is a conscious decision made during study planning. A systematic review of psychosocial interventions with substance use disorders also found studies gave little justification for control group choice or considerations for how this choice may have affected study outcomes [ 24 ].

The classic work of Campbell and Stanley identifies the Solomon 4 group design as offering the greatest internal and external validity checks [ 5 ]. This design includes these groups: experimental (pretest-intervention-posttest), no pretest experimental (intervention-posttest), control (pretest-posttest), no pretest control (posttest). Comparison of posttest scores across the 4 groups reveals whether changes are the result of the intervention and/or learning from the test [ 5 , 79 ]. None of the reviewed studies had non-pretested comparison groups. This lack of control for testing may have important implications; indeed researchers note that repeated measurements may encourage control condition participants to reflect on behaviors and initiate the behavior targeted in the experimental condition [ 83 , 84 ]. Another research group suggested that the research design for psychosocial treatments that most closely equates to a double-blind design is one that compares “two bona fide interventions … delivered by advocates for those interventions” [ 24 ],p. 426–427. In the reviewed studies, just one study met these criteria [ 57 ]. That is, Wolf et al. reported that immigrant men were given either a fruit/vegetable or prostate cancer prevention brochure [ 57 ]. Both groups received 2 tailored telephone education calls that could be considered to be delivered by an “advocate” because callers use a standardized telephone protocol and were audiotaped as a check for fidelity of delivery (however, no mention was made as to whether different callers were used for each treatment). Still another research group felt that to disentangle effects of the “active ingredient” from effects of non-specific factors, studies should include 3 groups: wait list control, attention control, and experimental group [ 85 ]. Many of the reviewed studies had 2 of these groups, but none had all 3.

Dismantling designs make it possible to separately account for the effects of each intervention component. However, the reviewed dismantling studies were mostly additive—that is, the treatment groups received increasingly intensive treatments thereby making it impossible to ascertain whether it was the greater dose of the additive treatment that contributed to changes or just the additional element [ 46 , 54 , 58 , 63 , 65 ]. For instance, one had a control group who received 12 weekly non-tailored newsletters by mail, an experimental group received 12 weekly tailored newsletters by mail, and another experimental group received the 12 weekly tailored newsletters plus weekly home visits from a promotora (lay health advisor/counselor) [ 64 ]. There was not a group who received only promotora visits, thus differentiating between intensity and independent effects of the promotora was not possible.

In the words of Montgomery et al., “[p]oor reporting limits the ability to replicate interventions, synthesise evidence in systematic reviews, and utilise findings for evidence-based policy and practice. The lack of guidance for reporting the specific methodological features of complex intervention RCTs contributes to poor reporting” [ 86 ],p.99. To improve reporting, the CONSORT extension underway for randomized controlled trials of social and psychological interventions may be appropriate and/or adaptable for health and nutrition education and promotion programs [ 86 ]. Methods for overcoming deficiencies in reporting design and execution of both control and experimental conditions reported by others may serve as models for reporting nutrition education interventions [ 7 , 87 ]. One research group has even suggested creating a repository of treatment descriptions, citing the Centers for Disease Control and Prevention’s Replicating Effective Programs ( https://www.cdc.gov/hiv/research/interventionresearch/rep/index.html ) as an example, and establishing a detailed checklist of characteristics to be included in intervention descriptions [ 26 ]. In fact, the supplementary table published by Greaves and colleagues is an excellent reporting method that ensures all salient elements are included [ 87 ]. Table 3 in this paper is another tool for ensuring key information is reported in nutrition education outcomes papers.

Strengths of this review lie in the large number of papers included and the extensive extraction of data contributing to this comprehensive description of control groups in nutrition education interventions and how they and experimental conditions are recounted in peer-reviewed journals. Additionally, it is the first study to explore control conditions in nutrition education and is among the first in any field to examine this critically important research intervention study design and reporting component [ 7 , 26 , 85 ]. This study is, however, limited to studies conducted in the United States. Furthermore, the studies reviewed likely included at least some of the extracted factors reported as missing in Additional file 1 : Table S5, but did not explicitly report them in the published paper. Also, no attempt was made to examine cited sources, which may supplement the information provided in the reviewed papers. Examination of the appropriateness of outcome measures, adequacy of sample sizes, and effect of control condition on study outcomes were beyond the scope of this review, but are important targets for future investigations.

Calls for more transparency and detail in reporting interventions have occurred sporadically since at least 1991, yet little has changed [ 77 , 88 ]. In this day and age of ever constricting research funding, coupled with the dire need for interventions that effectively improve nutritional status and associated outcomes, it is imperative that intervention research use more robust study designs that permit us to understand the effects of each component of the intervention [ 26 , 85 ]. Additionally, researchers and journal editors should assume the responsibility for ensuring that practitioners can easily access the details needed to implement effective interventions with fidelity. The key historic barrier to reporting this data in printed form has been overcome with electronic publishing [ 26 , 89 ]. Clearly there is a great deal of opportunity to improve intervention study design and reporting—seizing this opportunity can only help to advance the field and improve consumer health. A goal set at the outset of the investigation reported here is to open a dialogue among nutrition education researchers that leads to improved reporting of control and experimental condition treatments in intervention evaluation studies to promote advancement and impact of our work.

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Byrd-Bredbenner, C., Wu, F., Spaccarotella, K. et al. Systematic review of control groups in nutrition education intervention research. Int J Behav Nutr Phys Act 14 , 91 (2017). https://doi.org/10.1186/s12966-017-0546-3

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A systematic review of types of healthy eating interventions in preschools

• Mette V Mikkelsen 1 ,
• Sofie Husby 1 ,
• Laurits R Skov 1 &
• Federico JA Perez-Cueto 1  

Nutrition Journal volume  13 , Article number:  56 ( 2014 ) Cite this article

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With the worldwide levels of obesity new venues for promotion of healthy eating habits are necessary. Considering children’s eating habits are founded during their preschool years early educational establishments are a promising place for making health promoting interventions.

This systematic review evaluates different types of healthy eating interventions attempting to prevent obesity among 3 to 6 year-olds in preschools, kindergartens and day care facilities. Studies that included single interventions, educational interventions and/or multicomponent interventions were eligible for review. Included studies also had to have conducted both baseline and follow-up measurements.

A systematic search of the databases Scopus, Web of Science, CINAHL and PubMed was conducted to identify articles that met the inclusion criteria. The bibliographies of identified articles were also searched for relevant articles.

The review identified 4186 articles, of which 26 studies met the inclusion criteria. Fifteen of the interventions took place in preschools, 10 in kindergartens and 1 in another facility where children were cared for by individuals other than their parents. Seventeen of the 26 included studies were located in North America, 1 in South America, 5 in Asia, and 3 in a European context.

Healthy eating interventions in day care facilities increased fruit and vegetable consumption and nutrition related knowledge among the target groups. Only 2 studies reported a significant decrease in body mass index.

Conclusions

This review highlights the scarcity of properly designed healthy eating interventions using clear indicators and verifiable outcomes. The potential of preschools as a potential setting for influencing children’s food choice at an early age should be more widely recognised and utilised.

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Introduction

The worldwide prevalence of overweight and obesity among preschool children has increased from 4.2% (95% CI: 3.2%, 5.2%) in 1990 to 6.7% (95% CI: 5.6% – 7.7%) in 2010 and is expected to increase even further to 9.1% (95% CI: 7.3 – 10.9) in 2020 [ 1 ]. This increase is disturbing due to the accompanying social, psychological and health effects and the link to subsequent morbidity and mortality in adulthood [ 2 , 3 ].

Considering the consequences of overweight and obesity on both a personal and societal level, healthier eating habits among children should be promoted as one of the actions to prevent overweight and obesity in future generations. The most common place for health promotion among children has previously been in the school setting mostly with children aged 6 to 12 years-old. But, there are promising findings in interventions targeting infants and 5-year-olds, although there is an underrepresentation of interventions and research within this age group [ 4 ]. Most of these interventions have been taking place in early education establishments for 3–6 year-olds like preschools in the U.S. or kindergartens as they are called outside the U.S. as well as daycare facilities, where children are nursed by a childcare giver in a private home. In this setting children consume a large number of their meals and may consume up to 70% of their daily nutrient intake [ 5 ]. These captive settings present a venue for intervention because institutional catering may be designed in such a way that nutritional guidelines are followed, resulting in an adequate food intake [ 6 ] and improved food choices later in life [ 7 ]. The objectives of the early educational establishments are often to teach and develop the child’s opportunities and skills that will prepare them for a better future [ 8 ] and many of the previous interventions have either focused on developing food preferences among children often by exposure or with nutritional educational interventions or with a combination of these two approaches. Previous reviews have included intervention studies that evaluated the outcomes of dietary educational interventions versus control on changes in BMI, prevalence of obesity, rate of weight gain and other outcomes like reduction in body fat, but as stated previously this did not yield a sufficient number of studies to provide recommendations for practice [ 4 , 9 ]. The Toybox study [ 10 ] has published a number of reviews about several aspects of health promotion efforts for pre-schoolers including the assessments tools of energy-related behaviours used in European obesity prevention strategies [ 11 ], the effective behavioural models and behaviour change strategies underpinning preschool and school-based prevention interventions aimed at 4-6-year-olds [ 12 ]. They also published a narrative review of psychological and educational strategies applied to young children’s eating behaviour in order to reduce the risk of obesity and found that there was potential for exposure and rewards studies to improve children’s eating habits [ 13 ]. None of the recent published studies have included both interventions that include both exposure or meal modification and educational interventions and multicomponent interventions that combine both approaches. With the exception of [ 13 ] all the previous reviews include physical activity and although this is an important factor in obesity prevention, many interventions do only focus on nutritional education and is as such excluded from previous reviews.

The objective of this article is to review published literature on healthy eating interventions in day care facilities and analyse the effectiveness of different strategies in relation to their influence on children’s food choice at an early age. Based on findings, this article also provides recommendations for future interventions.

A systematic search for literature using four databases (PubMed, Scopus, Web of Science and CINAHL) was carried out. The search strategy was based on a careful selection of keywords and clear, pre-established criteria for inclusion of studies.

Inclusion criteria

Included studies were intervention studies with the objective of treating or preventing the occurrence of obesity by influencing preschool children’s eating habits. As a prerequisite for inclusion, the healthy eating interventions had to take place in institutions and had to have taken both baseline and follow-up measurements. Although it is acknowledged that physical activity interventions are important and should not be disregarded, this study focuses solely on healthy eating interventions. Only studies targeting children aged 3 to 6 years were included as it is this age group that predominantly attends early education facilities. Since early education and school systems vary from country to country, it was decided to include all interventions in day care facilities if the mean age was between 3 to 6 years old. Children in included studies also had to be healthy at initial baseline measurement, although obese children were included in order to recognize the already existing prevalence of overweight and obesity in children and the necessity to acknowledge treatment of this particular target group. Interventions that focused on diet, nutrition, food, eating or meals in day care facilities were included. Due to the importance of environmental factors in children’s acquirement of healthy eating habits, interventions including kitchen employees and childcare givers in day care facilities were also included. As the review concerns itself with the effectiveness of different interventional strategies, the types of interventions were categorized into single component interventions, educational components, and multicomponent interventions that aiming to promote healthy eating habits and counteract obesogenic actions in children attending day care facilities.

The review included studies measuring biological, anthropometric and attitudinal outcomes: body mass index, z-scores for height and weight, waist to height measurements, serum cholesterol levels, skin-fold measurements or prevalence of overweight and obesity in the sample population, as well as food consumption patterns, knowledge and attitude towards foods and liking and willingness to try new food.

Exclusion criteria

Research into weight loss of obese children and any interventions involving children with special needs or who were chronically sick and required on-going counselling, such as patients with diabetes or heart disease, were excluded from the review. Studies taking place in nursery, primary or elementary schools were also excluded when the mean age was either younger than 3 years or older than 6 years old. Interventions targeting parents of preschool children and descriptive articles about pre-schoolers behaviour, knowledge and consumption were also excluded. Lastly, studies including a physical activity component were excluded unless the dietary component was clearly separated from the physical activity intervention during implementation and analysis.

Conducting the search

Literature for the review was obtained using a systematic search conducted during spring 2014 with relevant literature published up to and including the search period. A meta-analysis was intended, however due to a lack of sufficient data, a meta-analytical comparison was difficult to deploy.

The databases Scopus, Web of Science, CINAHL and PubMed databases were used for the literature search. The search was restricted to articles written in English, German, Norwegian, Swedish, and Danish as these were the language capabilities present in the reviewing group. The filter for research involving humans only was activated and the search was conducted to obtain articles published between 1980 and 2014.

The search strategy was created using relevant terms describing settings, possible inputs in an intervention and possible outputs of an intervention. The search terms were refined a number of times in order to optimize the selection of articles, without compromising with the sensitivity of the search in order to take into account the vast number of articles published on the topic of children and obesity. The keywords can be found in Table  1 .

Data management

The search hits were downloaded and saved in the databases. A total of 4186 papers were identified and screened on the basis of titles and abstracts by the first author, who has experience within a preschool venue, leaving 66 papers for further enquiries. Reference lists from the systematic review were scanned in order to identify interventions in kindergartens and preschools that the previous search had been unable to detect. Altogether, 10 papers were identified. After removing repeated studies and articles, 47 full text papers were retrieved through the library service at University of Aalborg, campus Copenhagen.The 47 remaining papers were read independently by three reviewers in order to verify that they met the inclusion criteria. 33 papers were excluded as a consequence primarily because they did not publish results, solely was targeted parents or were descriptive in nature. The reviewing process resulted in 26 papers left for analysis. Figure  1 contains an overview of the search process.

Flowchart of the study selection process.

Data collection and analysis

Selection of studies.

Articles identified in the literature search were read by the first author and divided between three reviewers for further evaluation and was debated in meetings with all three reviewers present.

For each of the located interventions, the following was extracted: aim of the study, setting where 3–6 year-olds were cared for by others than their parents, study design, characteristics of the target group, sampling methods, sample size, ethnicity, and theoretical background. Furthermore; duration, content and delivery mechanism of the intervention was extracted, as well as information about the control group, random allocation to control or treatment and whether there was information missing from the article.

Quality assessment

The quality of the identified studies was assessed using a rating scheme from * (weak) to **** (very strong). The studies were rated according to the level of information available, study design, risk of bias, study population and study duration. The quality rating scheme was adapted from the Cochrane guidelines on quality assessment [ 14 ]. Table  2 illustrates definition and explanation of the research design rating scheme. Each included study was rated independently among the three first authors (MVM, SH & LRS) with strong inter-rater reliability and disputes over assessment were settled through discussion.

The 26 studies that the literature search resulted in were divided into 8 single intervention studies, 11 educational interventions and 7 multicomponent studies. The single intervention studies involved the modification of a single factor in the environment in order to promote fruit or vegetable intake and preferences in children. Educational interventions were carried out in the kindergartens, either by teachers that had undergone a teaching program or by nutritional educators provided by the research program and aiming to increase children’s knowledge of healthy eating. Multicomponent interventions included more than one strategy to influence eating behaviour.

Table  3 shows the characteristics of the studies.

Populations studied

Altogether, 17 of the 26 included studies were North American, three of the studies were carried out in Asia, five in a European context and one study was conducted in South America. Thirteen of the interventions took place in preschools, 10 in kindergartens and three in other facilities where 3 to 6 year-olds were cared for by others than their parents.

Ethnicity and socio-demographic characteristics of participants

The majority of the single interventions was from the USA and included Caucasians. The educational interventions did not present a clear picture of any tendencies. All of the American multicomponent interventions were targeted towards low-income families or families from African-American or Latino backgrounds. The European interventions targeted children from middleclass families.

Interventions

Of the single intervention studies identified the majority [ 10 , 17 – 20 , 22 ] made modifications to the serving of vegetables, serving either novel or non-preferred vegetables and looked at the effect on vegetable preferences as well as whether peer-models had an influence on the children’s intake during lunch.

We identified eleven interventions consisting of nutritional educational programs carried out either by teachers in the kindergarten, individuals that had undergone a training program or by nutritional educators provided by the research project.

Seven multicomponent interventions included educational activities for the children and delivered similarly to the educational activities described previously. The multicomponent interventions also encompassed other activities like availability of fresh water and fruits and in some cases vegetables [ 8 , 36 , 39 ] the children participation in growing their own vegetables [ 22 , 37 ], newsletters for parents [ 36 , 41 ], food modifications in the canteen[ 42 ] and healthy school policies [ 41 ]. A detailed description of the interventions can be found in Table  3 .

Table  4 shows the quality assessment and outcomes of interventions.

The study design of included studies

Fourteen of the 26 studies included in this review were randomized controlled studies or cluster randomized controlled trials. Nine quasi-experimental designed studies were found primarily as single or educational intervention [ 20 – 23 , 29 , 32 – 34 , 42 ]. Only one study used a crossover design as control [ 19 ], but neither the sampling method nor the time between intervention and control were stated, making the control effect limited.

Sampling methods

Random sampling had been used in only five of the 26 studies as most of the studies were based on convenience sampling. Two studies combined random and convenience sampling [ 22 , 34 ]. Four studies did not describe the sampling method used [ 19 , 23 , 30 , 31 , 35 ].

Sample size

Sample sizes varied greatly between single interventions and the educational and multicomponent interventions. The mean sample size of the single component interventions was 78 and the mean sample size among the educational and multicomponent interventions were 1031 and 522. The mean sample size of all 26 studies was 601.

Main target behaviours

Food preferences, willingness-to-try novel foods and nutrient intake during lunch were the most used target behaviours in the single interventions. Not surprisingly knowledge and attitudes were the most used target behaviours in the educational interventions, but also consumption of target foods were evaluated using food frequency questionnaires answered by parents. The consumption of target foods were also evaluated in multicomponent studies, but here the intake was measured using observation by researchers or teachers in the setting, just as it was the case for single interventions. Anthropometric measurements of height and weight were applied across the studies, although they only happened in two single interventions [ 19 , 20 ], however it was only used to control for BMI in the statistical analysis. The multicomponent interventions included other anthropometric measures as well.

Duration of intervention

The single change interventions were relatively short in duration, lasting from 3 to 4 days and up to 6 weeks. The educational interventions with a smaller sample size lasted from 5 to 8 weeks and the studies involving a higher number of participants were of longer duration of between 10 months to 2 years. However, there were exceptions to this, including Cason [ 25 ] who evaluated a preschool nutrition program involving 6102 children over 24 weeks and Parcel et al., [ 32 ] who carried out a 4 year study targeting approximately 200 preschool children Hendy [ 20 ] failed to report their intervention duration. The duration of the multicomponent interventions was generally between 4 and 7 months and up to 1 year.

Theoretical foundations of interventions

16 of the 26 included interventions did not base their interventions on health behavioural theories. 6 of the studies used Bandura’s social cognitive theory or the related social learning theory. Piaget’s developmental theory was used in 2 studies and others were the theory of multiple intelligences or Zajonc’s exposure theory.

Information missing from articles

In the single interventions Hendy [ 20 ] failed to state the duration of their intervention and Ramsey et al. [ 23 ] did not mention their allocation process, however this was due to the study taking place at one canteen without individual data. Nemet et al. [ 30 ] and Witt et al. [ 35 ] failed to report their sampling process, which was quite surprising considering the high research rigour their studies otherwise presented.

The single interventions generally had small sample sizes, lacked controls and were of relatively short duration and with a short period of time in-between the exposure and follow-up measurements and. The majority of studies in both the educational and multicomponent intervention groups suffered from low response rates.

Effects of interventions

Single intervention.

Single exposure interventions failed to demonstrate a significant increase in vegetable consumption. Fruit intake was more easily influenced, however. Results also showed that younger children in particular were influenced by role models and that girls may be more promising role models than boys [ 17 , 18 ].

Educational intervention

None of the educational interventions resulted in a change in anthropometric measurements, with the exception of [ 30 ] who observed a significant decrease in children’s BMI in the overweight children group who became normal weight. At follow-up after one year the BMI and BMI percentiles were significantly lower in the intervention group compared to the control group. Promising results were also found in 6 of the studies where an increase in the consumption of fruit and vegetables was observed. However, none of these changes were significant at the 0.05 level, with the exception of [ 35 ], where a significant increase was found in the consumption of fruit by 20.8% and in vegetable snacks by 33.1%. Witt et al. [ 35 ] found a significant increase in vegetables served outside preschools, but this was based on mother’s own food frequency data, which may have biased the results [ 33 ]. One of the major effects of the educational interventions was in the level of knowledge among its participants. For instance, the level of nutrition-related knowledge increased in two studies [ 24 , 30 ] and the identification of fruits and vegetables increased in two studies [ 25 , 27 ].

Multicomponent interventions

Six of the multicomponent interventions showed a significant increase in fruit and vegetable consumption, but one found the effect only to be present on fruit consumption after follow-up after 1 year. None of the other studies found an effect on BMI, but one intervention resulted in a decrease in the relative risk of serum cholesterol among children [ 42 ]. Only one study [ 39 ] evaluated knowledge and found that familiarity with novel foods increased significantly.

Discussion and conclusions

This review finds that healthy eating interventions can influence the consumption of vegetables through different strategies. The studies acknowledged that a single exposure strategy was insufficient to increase vegetable consumption and that there needs to be an education component as well. This was supported by the fact that the over half of the educational interventions and six of the eight multicomponent interventions resulted in an increase in vegetable consumption. The increase in consumption was greater in the multicomponent studies which could indicate that the more comprehensive the intervention strategy, the more likely the intervention is to be successful.

The effectiveness of the interventions on anthropometric change was more inconclusive, the single interventions did not include measures of BMI and considering how short the duration of their interventions were, it might also be difficult to find change in anthropometric measures. None of the other intervention types that did in fact use anthropometric measurements found an effect on BMI, with the exception of [ 31 ]. However Witt et al. [ 35 ] found an effect on serum cholesterol.

The educational and the majority of multicomponent interventions included an educational component and the former did find significant increases in nutrition related knowledge, but the multicomponent interventions did not evaluate intermediate effects of knowledge in addition to anthropometrics. This highlights the fact that multicomponent interventions should include measures on knowledge, when they include an educational component, particularly, because the duration of multicomponent interventions often was shorter than the pure educational interventions and anthropometric change is difficult to find during short intervention periods. A lack of follow-up in all of the interventions makes it difficult to conclude whether the observed effects were sustainable over time. With the exception of De Bock et al. [ 38 ] and Hoffman et al. [ 40 ] the multicomponent and even some of the educational intervention failed either to base or mention the theoretical foundations that they based their educational programmes on. This may be excused in the single interventions that base their studies on empirical data from food choice development theories, but interventions aiming at delivering educational programmes should have some knowledge of health behavioural or educational theories that explains the process behind the success or failure of the implementation of their educational programs. This is again highlighted by the fact that process evaluations were only performed in three of the interventions and the evaluations consisted of either revision of the provided educational materials or checking the adherence to the program, but they did not focus on drivers or barriers behind the implementation of the interventions and thereby to increase the understanding of what made the intervention successful or unsuccessful.

Ethnicity and socio-demographic background play an important role in the development of eating habits and this should be taken into account so interventions are targeted towards those that need it the most. A setting-based approach can be an important intermediate for this, if it is applied to institutions where children of low-income families are nursed and educated. Several educational and multicomponent interventions were targeted towards institutions with children of low-income families and several of them e.g. Cespedes et al. [ 26 ], Vereecken et al. [ 41 ], and Williams et al. [ 42 ] had positive results especially on the consumption of fruits and vegetables that supports the notion of early education establishments as a potential setting to decrease inequalities in health.

Quality of the evidence

Overall the quality of the intervention studies became better the more comprehensive they were; the single intervention studies were generally of weak quality with small sample sizes, short durations and, in some cases, a lack of controls, which makes it difficult to generalize to a larger population, especially because they were mostly carried out among American Caucasians from families with high socio-economic status. The educational interventions were of better quality and with the largest populations, but still suffered from limitations like lack of consideration in the allocation process, in some cases lack of controls and high drop-out rates. The multicomponent interventions were the most well-designed studies, but also suffered from high drop-out rates and as mentioned above the effectiveness of the educational components were difficult conclude upon, because they failed to evaluate on knowledge. With the exception of Nemet et al. [ 31 ] there was a lack of follow-up evaluations that makes it difficult to state whether the outcomes of interventions are sustainable over time.

Author’s conclusions

Implications for practice.

The majority of interventions found promising results when targeting the consumption of healthy foods or when attempting to increase children’s knowledge of healthy eating, providing sufficient evidence in support of using preschools as a setting for the prevention of chronic disease by making behavioural and lifestyle changes. Interventions are more likely to be successful if they take actions on several levels into account.

Implications for research

This review supports the need for a longer follow-up of intervention studies in order to assess whether results will be sustainable and how they might influence children’s eating habits later in life. Anthropometric measurements were included in some of the multicomponent interventions but as nutritional status measured as BMI does not change rapidly, interventions using BMI as the outcome measure should be of a longer duration or they should include other intermediate measures such as knowledge and consumption in order to evaluate the effectiveness of the intervention.

Parents may not always be aware of what their children consume outside of the home, or about their knowledge surrounding fruits and vegetables, particularly when children learn about food and healthy eating behaviour in their kindergartens. Even though many choices are made on behalf of the children by their parents at home, children today spend a reasonably large amount of time away from the home environment in day care facilities, together with playmates or cared by other members of family. As a result, a child’s food choice is no longer restricted to being a sole family matter. Children’s knowledge and awareness of food is also being influenced in pedagogical activities, in day care facilities or by talking to their peers. It would therefore be suitable to develop innovative data collection methods, ensuring that the children are able to express what they like to eat and what they know about a given food-related topic. Such innovative research methods should take the developmental stages of the children into account and could perhaps rely more heavily on pictures or on IT material.

The review found that healthy eating interventions in preschools could significantly increase fruit and vegetable consumption and nutrition-related knowledge among pre-school children if the strategy used, is either educational or an educational in combination with supporting component. It further highlights the relative scarcity of properly designed interventions, with clear indicators and verifiable outcomes. Key messages are that preschools are a potentially important setting for influencing children’s food choice at an early age and that there is still room for research in this field. Healthy eating promotion efforts have previously been focusing on schools, but within the last decade the focus have started to shift to pre-schoolers. This review synthesizes some of the interventions that promote healthy eating habits on early education establishments using different strategies. The field of health promotion among this younger age group is still in its earlier stages, but future studies with thorough research designs are currently being undertaken like the Toybox study [ 10 ] and The Growing Health Study [ 43 ], the healthy caregivers-Healthy children [ 44 ] and the Program Si! [ 45 ]. These studies may improve our understanding of the effectiveness and underlying mechanisms behind successful implementation of healthy eating efforts in early education establishments.

Healthy eating interventions in preschools were classified by their type.

Comprehensive interventions were more likely to succeed in behaviour change, especially when targeting children of low-income families

Preschools are a promising venue for increasing fruit and vegetable consumption.

Evaluations showed a positive increase in food-related knowledge.

Properly designed interventions, with clear indicators and outcomes are scarce.

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Mikkelsen, M.V., Husby, S., Skov, L.R. et al. A systematic review of types of healthy eating interventions in preschools. Nutr J 13 , 56 (2014). https://doi.org/10.1186/1475-2891-13-56

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Weight Loss in Short-Term Interventions for Physical Activity and Nutrition Among Adults With Overweight or Obesity: A Systematic Review and Meta-Analysis

SYSTEMATIC REVIEW — Volume 21 — April 4, 2024

Wendi Rotunda, PhD 1 ; Caroline Rains, MPH 1 ; Sara R. Jacobs, PhD 1 ; Valerie Ng, BS 1 ; Rachael Lee, MSPH 1 ; Stephanie Rutledge, PhD 2 ; Matt C. Jackson, PhD, MPH 3 ; Kristopher Myers, PhD 2 ( View author affiliations )

Suggested citation for this article: Rotunda W, Rains C, Jacobs SR, Ng V, Lee R, Rutledge S, et al. Weight Loss in Short-Term Interventions for Physical Activity and Nutrition Among Adults With Overweight or Obesity: A Systematic Review and Meta-Analysis. Prev Chronic Dis 2024;21:230347. DOI: http://dx.doi.org/10.5888/pcd21.230347 .

PEER REVIEWED

Introduction

Acknowledgments, author information.

What is already known on this topic?

Long-term lifestyle change programs can be effective at achieving weight loss for adults with overweight or obesity and can lower their risks for developing chronic diseases, such as type 2 diabetes. However, enrollment and retention are challenging in long-term interventions.

What is added by this report?

We demonstrated that multicomponent nutrition and physical activity interventions of 6 months or less can achieve weight loss by the end of the intervention period.

What are the implications for public health practice?

Short-term lifestyle change programs can produce weight loss that may be associated with reduced risk of chronic diseases. Providing both short-term and long-term options could increase enrollment in such programs.

Reaching, enrolling, and retaining participants in lengthy lifestyle change interventions for weight loss is a major challenge. The objective of our meta-analysis was to investigate whether lifestyle interventions addressing nutrition and physical activity lasting 6 months or less are effective for weight loss.

We searched for peer-reviewed studies on lifestyle change interventions of 6 months or less published from 2012 through 2023. Studies were screened based on inclusion criteria, including randomized controlled trials (RCTs) for adults with overweight or obesity. We used a random-effects model to pool the mean difference in weight loss between intervention and control groups. We also performed subgroup analyses by intervention length and control type.

Fourteen RCTs were identified and included in our review. Half had interventions lasting less than 13 weeks, and half lasted from 13 to 26 weeks. Seven were delivered remotely, 4 were delivered in person, and 3 used combined methods. The pooled mean difference in weight change was −2.59 kg (95% CI, −3.47 to −1.72). The pooled mean difference measured at the end of the intervention was −2.70 kg (95% CI, −3.69 to −1.71) among interventions lasting less than 13 weeks and −2.40 kg (95% CI, −4.44 to −0.37) among interventions of 13 to 26 weeks.

Short-term multicomponent interventions involving physical activity and nutrition can achieve weight loss for adults with overweight or obesity. Offering short-term interventions as alternatives to long-term ones may reach people who otherwise would be unwilling or unable to enroll in or complete longer programs.

Approximately 60% of US adults have a chronic disease, and approximately 40% have 2 or more (1). Chronic diseases are a leading cause of death and disability (2) and contribute substantially to the $3.8 trillion in annual health care costs in the US (1). Multicomponent lifestyle change programs are known to be effective in reducing the risk of developing chronic diseases and largely focus on losing weight (3,4). Weight loss is an important objective for many lifestyle change interventions given the increased risk for people with overweight or obesity to develop chronic diseases, including type 2 diabetes (5), cardiovascular disease (6), and cancer (7). However, enrolling and remaining in such interventions are a challenge, particularly for those of longer duration (8,9). Thus, short-term interventions may have the potential to both enroll more participants and achieve higher retention (8,9). In addition, evidence indicates that most people achieve their greatest weight loss in the first 3 to 6 months of a lifestyle change intervention (10).

Previous systematic reviews examined interventions of various lengths for weight change (11–14) but did not look at whether the intervention length itself substantially affected body weight. Although 1 prior meta-analysis examined weight change in an intervention that lasted 6 months or less compared with 12 months or more, the study’s population was specific to adults with overweight or obesity who were also diagnosed with a mental illness (15). That analysis found, however, that the weight change effect size was similar in interventions of 6 months or less compared with interventions of 12 months or more.

Although weight loss is associated with preventing or delaying the onset of chronic conditions (3,4), long-term interventions have challenges in enrolling and retaining participants (8,9). We sought to understand the extent to which multicomponent interventions of 6 months or less were effective at achieving weight loss among adults with overweight or obesity.

Data sources

We searched PubMed via Medline, Web of Science, APA PsycInfo, Embase, CINAHL, and Cochrane Library for peer-reviewed studies on lifestyle change interventions of 6 months or less that were published from January 2012 through January 2023. We selected these years to ensure that the use of technology that might affect intervention length in the delivery of these interventions was reflected in the studies. Our search strategy ( Table 1 ) used a combination of key terms including 1) a health condition or lifestyle and behavior term (eg, physical activity, overweight), 2) a program or intervention term (eg, lifestyle change, intervention), and 3) an outcome term (eg, weight loss). We also hand-searched systematic reviews identified in the searches.

Study selection

We included peer-reviewed primary research studies published in English that reported on lifestyle change interventions of 6 months or less (operationalized as 26 weeks) for adults aged 18 years or older with overweight or obesity. Studies had to report weight loss outcomes to be eligible for inclusion. We excluded studies in which participants were already diagnosed with a chronic condition, such as hypertension or diabetes, but included studies that were intended for populations with heightened risks for developing chronic conditions. We also excluded studies without an intervention component focused on nutrition or physical activity.

We included randomized controlled trials (RCTs) only and excluded other study designs, such as observational studies, given that other designs are more susceptible to bias or confounding, and studies that did not conduct an intention-to-treat analysis, because complete case analysis may lead to bias in the intervention effect estimates (16). Studies also had to be conducted in countries rated as very high in development based on the United Nations Human Development Index (17), so that findings would be more generalizable to US adults with overweight or obesity.

For studies with multiple intervention arms, we selected a primary arm to include in the analysis. We selected the primary intervention arm based on several factors, such as the intervention included either nutrition or physical activity with the goal of weight loss (some of the alternative interventions did not include a lifestyle change component) or the intervention included multiple methods such as in-person sessions and an online forum meant to maximize participation and retention. In cases where multiple intervention arms met the above criteria, we included 1 intervention arm in the main analysis and the other intervention arm in a sensitivity analysis.

Data extraction and critical appraisal

We used Covidence Systematic Review Software (Veritas Health Innovation) to help manage the systematic review process. Two team members used the study selection criteria to independently review each title and abstract. All conflicts at the title and abstract stage were advanced to the full-text review. Full-text articles were also reviewed independently by 2 reviewers. Conflicts were resolved by a third senior reviewer, who also confirmed inclusion of all final articles.

Reviewers used a standardized extraction form to extract key data. The extraction form was programmed in REDCap (REDCap Consortium) (18,19), and each article was extracted by one reviewer and checked for accuracy by a senior reviewer. Data on body weight change were extracted in the reported units, either kilograms or pounds, and then standardized into kilograms for all studies. We used the National Heart, Lung, and Blood Institute’s Study Quality Assessment Tool ( Box ) (20) to document the methodologic quality of the included studies. Studies were scored and classified as poor (0–5 points), fair (6–12 points), or high (13,14 points). All 5 reviewers were trained on the extraction and study quality assessment tools before they completed the full-text extractions.

Box. National Heart Lung, and Blood Institute’s Study Quality Assessment Tool (https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools)

Answer options are yes, no, neither (cannot determine, not reported, or not applicable).

1. Was the study described as randomized, a randomized trial, a randomized clinical trial, or an RCT?

2. Was the method of randomization adequate (ie, use of randomly generated assignment)?

3. Was the treatment allocation concealed (so that assignments could not be predicted)?

4. Were study participants and providers blinded to treatment group assignment?

5. Were the people assessing the outcomes blinded to the participants’ group assignments?

6. Were the groups similar at baseline on important characteristics that could affect outcomes (eg, demographics, risk factors, comorbid conditions)?

7. Was the overall dropout rate from the study at endpoint 20% or lower of the number allocated to treatment?

8. Was the differential dropout rate (between treatment groups) at endpoint 15 percentage points or lower?

9. Was there high adherence to the intervention protocols for each treatment group?

10. Were other interventions avoided or similar in the groups (eg, similar background treatments)?

11. Were outcomes assessed using valid and reliable measures, implemented consistently across all study participants?

12. Did the authors report that the sample size was sufficiently large to be able to detect a difference in the main outcome between groups with at least 80% power?

13. Were outcomes reported or subgroups analyzed prespecified (ie, identified before analyses were conducted)?

14. Were all randomized participants analyzed in the group to which they were originally assigned (ie, did they use an intention-to-treat analysis)?

Statistical analysis

We used the mean body weight change from baseline to the end of the intervention time point for both the intervention and comparison groups. When these data were not reported, we used other data provided in the study for calculating the change (21). We used Stata, version 17 (StataCorp LLC) to calculate the pooled mean difference in weight change (in kilograms) by using a random effects model with the inverse variance weighting method described by DerSimonian and Laird (22).

We assessed statistical heterogeneity (ie, variability resulting from differences in the study effects) in pooled estimates by examining I 2 statistics and P values. We considered I 2 values of 0% to 40% to indicate unimportant heterogeneity, 30% to 60% to indicate moderate heterogeneity, 50% to 90% to indicate substantial heterogeneity, and 75% to 100% to indicate considerable heterogeneity (23). When we observed moderate, substantial, or considerable heterogeneity (23), we conducted sensitivity analyses after removing outlier studies. We also visually examined plots for effects of different study characteristics and intervention factors, including the intervention method, proportion of female participants, average age of participants, average baseline weight of participants, and the percentage of participants completing the intervention.

Subgroup analyses were performed based on intervention length (<13 wk or 13–26 wk) and the type of comparison group described as low touch, usual care, or wait list. Low-touch comparison groups could entail a minimal amount of intervention for lifestyle change; for example, participants may have received informative emails (24) or printed information related to healthy habit formation (25). Usual-care groups were encouraged to engage in their regular behaviors without changing their usual routine. Wait-list or clinical-care comparison groups would eventually receive the intervention after data collection. We made the distinction between groups because a comparison group that included some engagement with participants could limit the ability to detect true intervention effects on weight loss compared with comparison groups that were considered usual care or were delayed in receiving the intervention.

Study characteristics

We screened 1,251 unique citations and identified 14 RCTs for inclusion in our review ( Figure 1 ). Among the 14 studies included, half had a wait-list comparison group (26–32), 5 had low-touch comparison groups (24,25,33–35), and 2 had usual-care comparison groups (36,37).

Five of the 14 studies were conducted in the US (26,28,32,34,35), 5 in Australia (27,29–31,36), 2 in the United Kingdom (33,37), 1 in Canada (25), and 1 in Turkey (24) ( Table 2 ). Seven studies had interventions lasting less than 13 weeks with a median of 12 weeks (24,25,27–31), and the other 7 studies lasted from 13 to 26 weeks, with a median of 24 weeks (26,32–37). The average age of study participants ranged from 40 to 52 years (24,25,27–31). Two studies included only women (28,35), and 3 studies included only men (29,30,32). Average baseline weight of study participants across all studies ranged from 82 kg to 139 kg. Seven of the 14 studies were delivered virtually, which included the use of websites, telephone, and email (24,29–31,34,36,37); 3 were a mix of both virtual and in-person components (27,32,35); and 4 were delivered exclusively in person (25,26,28,33). Among the 7 in-person and mixed-delivery intervention arms, 2 were conducted in a health care setting (28,33), 2 were conducted in a community setting (25,26), 1 was conducted in a university setting (27), 1 was conducted in the workplace (35), and 1 did not specify a setting (32).

For most of the 14 studies, the intervention focus was on both improving nutrition and increasing physical activity. Two interventions focused only on improving nutrition (34,35). Across the 14 studies that included a focus on nutrition, 7 described the nutritional component (28–30,32,34–36), 3 recommended a specific caloric value (eg, 1,200 calories per day) (28,29,34), 3 recommended participant-tailored guidance regarding calorie intake (30,32,35), and 1 allocated participants to a specific diet (36). Four studies described the physical activity component (28,32,33,36). One study reported structured group exercise in the form of 4 supervised circuit training sessions per week (28), and 3 reported providing participants with physical activity minute-count or step-count goals (32,33,36).

All 14 interventions also included more than 1 intervention component. For example, participants in 1 study (31) received a face-to-face information session, access to a study website to report daily diet and exercise, weight-loss education resources, a pedometer, and financial incentives. Another study (24) provided components that included access to an internet-based program, weekly lesson videos, food diaries, and both personalized and automatic messages. Eleven of the 14 studies provided participants with educational resources, such as booklets or access to information on a website (24,27–33,35–37); 10 studies provided participants with support tools, such as pedometers, scales, or access to food tracking logs (24,26–31,34,35,37); and 9 studies offered group or individual classes (25–27,31–35,37). Five studies supported participants by helping them set personalized exercise or calorie intake goals (29,30,32,33,35), 3 provided peer mentors or access to online discussion boards or forums where participants could work with other participants (26,30,36), and 3 sent automatic messages to participants that were not personalized, for example, messages reminding them to exercise or keep up their goals (24,30,32). In addition, 1 study provided financial incentives to participants who were part of a cohort that achieved the highest mean percentage weight loss after 1 month and at the end of the intervention (31).

Weight change

The pooled mean difference for weight change was less than −2.59 kg (95% CI, −3.47 to −1.72; 14 RCTs; 2,407 participants; I 2 = 69%) ( Figure 2 ). The negative difference in mean weight change indicates that people in the intervention groups lost more weight than those in the comparison groups. For the studies with interventions lasting less than 13 weeks, the pooled mean difference for weight change was −2.70 kg (95% CI, −3.69 to −1.71; 7 RCTs, 1,051 participants, I 2 = 73%). For the studies with interventions lasting 13 to 26 weeks, the pooled mean difference for weight change was −2.40 kg (95% CI, −4.44 to −0.37; 7 RCTs, 1,356 participants, I 2 = 69%) ( Figure 3 ). We conducted a moderator analysis with intervention duration and found a significant difference based on intervention duration ( P =.046).

Heterogeneity and sensitivity analyses

The pooled results had substantial heterogeneity overall and when stratified by intervention duration. We conducted a sensitivity analysis by removing studies with high attrition (> 20%) (24–26,33,35–37) ( Figure 4 ). From the 7 studies with interventions of less than 13 weeks, we dropped 2 low-touch comparison group studies with high attrition (24,25). Among the 5 remaining studies, heterogeneity improved ( I 2 = 0%, P =.91) and resulted in a larger mean difference for weight change: −3.48 kg (95% CI, −4.09 to −2.87). From the 7 studies with interventions of 13 to 26 weeks, we dropped 1 wait-list control study (26), 2 low-touch comparison group studies (33,35), and 2 usual-care comparison group studies (36,37) with high attrition. Among the 2 remaining studies (32,34), heterogeneity improved ( I 2 = 0%, P = .97) and resulted in a larger mean difference for weight change: −4.79 kg (95% CI: −6.30 to −3.25).

We also assessed heterogeneity by intervention method and participant characteristics. We examined results by delivery method (in person, online or other distance learning, or mixed) and gender of study participants (all men, >75% women, or a balanced mix of men and women). For results by delivery method, we found that the 7 studies conducted via online or other distance learning (24,29–31,34,36,37) had a larger effect size (−3.26 kg, I 2 = 75%) compared with the 3 studies conducted by using both online or other distance learning and in-person components (27,32,35) (−1.85 kg, I 2 = 60%), and the 4 studies conducted in person (25,26,28,33) (−0.84 kg, I 2 = 0%). We also found that for results by gender, the 3 studies conducted with all male participants (29,31,32) had a larger effect size (−3.82 kg) and minimal heterogeneity ( I 2 = 0%) compared with the 8 studies with more than 75% female participants (24–28,34–36) (−2.06 kg; I 2 = 69%) and the 3 studies with a more balanced mix of male and female participants (−3.16 kg; I 2 = 0%) (30,33,37). Baseline average age and baseline weight were similar across studies, so we do not report results by these subgroups. Intervention focus was also similar across studies (ie, most interventions focused on nutrition and exercise), so we do not report results by these characteristics.

We also conducted a sensitivity analysis that included alternative intervention arms for studies with more than 1 intervention group (25,27–30,34,36,37). The mean difference in pooled weight loss was slightly smaller than that of the selected primary intervention arm overall (−2.10 kg, 95% CI, −2.92 to −1.28) and by intervention time point (−2.16 kg; 95% CI, −3.02 to −1.31 for 13 weeks duration and −2.05 kg; 95% CI, −4.11 to 0 for 13–26 weeks duration). Heterogeneity was substantial overall ( I 2 = 69%) and for interventions of less than 13 weeks ( I 2 = 67%) and 13 to 26 weeks ( I 2 = 73%).

This meta-analysis of 14 RCTs found that interventions lasting 6 months or less were effective at achieving weight loss. Each study showed weight loss relative to control groups. The pooled mean difference in weight change was −2.59 kg compared with controls but may be further diminished when interventions are translated into real-world practice. However, adults with overweight and obesity tend to gain weight over time in the real world (eg, ~1% for >6 y) (38), such that lifestyle change interventions that slow or reverse weight gain trajectories are important in reducing the risk for developing chronic diseases. Thus, a key contribution of our study is bolstering the evidence that short-term lifestyle change interventions may result in weight change benefits in adults with overweight or obesity and could provide an alternative to longer interventions that some people may be unable or unwilling to complete (8,39,40). However, we do not know whether participants in these short-term interventions benefit, either in terms of weight change or chronic disease prevention. Our findings may have important health implications. Although the mean difference of approximately 2 kg among participants in the lifestyle change interventions relative to controls is modest, it can be clinically meaningful, because a lifestyle modification RCT reported a 16% reduction in 3-year diabetes risk for every kilogram of weight loss in the intervention group through lifestyle change (41).

All lifestyle interventions included in our meta-analysis were multicomponent, which may aid weight loss. This is consistent with findings from a recent meta-analysis where authors found that overall multicomponent lifestyle interventions were effective at achieving weight loss ranging from −1.3 kg to −8.2 kg at 5 to 6 months (42). The interventions included in that meta-analysis used various components to promote weight loss. The most frequent intervention components were educational resources, followed by support tools, such as pedometers and food and exercise diaries. Such components may facilitate self-monitoring of diet and body weight, which other studies have shown is a key to achieving healthy lifestyle behaviors (43) and preventing regain of weight lost (44). Our analysis did not examine which intervention components individually contributed to weight change. However, a recent systematic review and meta-analysis assessed the contribution of individual intervention components of lifestyle change programs, finding that change in diet, offering partial or total meal replacements, delivery by a psychologist–counselor or dietitian, and delivery in a home setting were associated with significant benefit in weight change (45). Additional research may be needed to disentangle the intervention components that drive weight change for interventions of shorter durations, such as the type of dietary guidance or the frequency and nature of physical activity recommendations. Additionally, future work should explore how social determinants of health, such as access to affordable and quality healthy food or safe places for physical activity, affect program and health outcomes (46). Understanding how different components of weight loss interventions can be adapted, tailored, or enhanced in response to contextual social determinants of health factors will help to ensure these types of interventions are equitable and accessible. Finally, 12 of the 14 included studies focused on improving both nutrition and physical activity to achieve weight loss. Therefore, we were unable to compare the effect of weight-loss interventions focused on nutrition alone versus physical activity alone. However, 1 systematic review and meta-analysis suggested that lifestyle change interventions that involved both diet and physical activity were associated with greater weight loss than those focused on diet (mean difference: –1.72 kg) or physical activity (mean difference: −5.33 kg) alone (47).

An important finding of our meta-analysis is that the interventions that lasted less than 13 weeks appear to be at least as effective for weight loss as those lasting from 13 to 26 weeks. One possible explanation for this finding is that interventions with a shorter duration showed a greater retention rate (~80%) than interventions of longer duration (~70%) in our analysis. This is consistent with other work that reported that programs of longer duration may experience higher dropout rates (48). In turn, high retention was important to increase weight loss from ~2 kg to ~4 kg in our sensitivity analysis where high attrition studies were removed, even when the intervention was relatively short in duration. This finding emphasizes that among interventions similar in length where higher retention is correlated with more significant weight loss (49,50), the success of these interventions also depends on sustained participant engagement. Future research should focus on determining which elements, such as personalized feedback or flexible scheduling, enhance retention.

Findings related to the effect of intervention duration in other meta-analyses are mixed. For example, 1 prior meta-analysis demonstrated that interventions lasting 12 months or more yielded slightly more weight loss for people with overweight or obesity compared with interventions lasting 6 months or less (15), whereas other meta-analyses reported no difference in weight loss by intervention duration (51,52). Nonetheless, interventions that require long-term engagement from participants may preclude some people from ever enrolling (53). Lengthier interventions can also be more challenging to disseminate and sustain because of the burden they place on the organizations that provide them (48).

Limitations

Our review has some limitations. First, in our meta-analysis we examined only weight change at the end of the intervention period and did not analyze any follow-up weight change that may have been reported; therefore, we could not make any conclusions about the ability of short-term interventions to sustain weight loss or reduce diabetes risk. Additional research could examine the effect of short-term interventions on sustained weight loss. Although 7 studies had substantial dropout rates at the end of the intervention (24–26,33,35–37), our sensitivity analysis showed that excluding these studies did not substantively change the overall findings. Also, given the multicomponent nature of nearly all the interventions we examined, we were unable to conclude which specific components are essential to driving weight loss. Future work should aim to disentangle the intervention components that may drive weight change for interventions of short duration. Although we improved heterogeneity by grouping studies according to their comparison group, weight loss possibly may be affected by other factors that vary between studies, such as different intensity and frequency of the interventions or differences in participant characteristics. For example, the included studies contained limited or no information on participant race or ethnicity and socioeconomic status, and some populations might respond differently to lifestyle interventions. Ensuring that interventions reduce existing health inequities is important but can be a challenge with long-term interventions that have resulted in better weight loss outcomes for participants who are non-Hispanic White and of higher socioeconomic status (39).

Short-term multicomponent interventions can possibly be effective in achieving clinically significant weight loss for adults with overweight or obesity. Participating in longer interventions may lead to more substantial results but may not be feasible for some people because of work schedules, caregiving responsibilities, transportation requirements, or other factors. Our findings can be used to inform a person’s decision making when offered a choice of programs, and by clinicians and researchers who can continue developing short-term alternatives to long interventions. Providing both short- and long-term options could increase opportunities for people to begin lifestyle changes and facilitate their choosing a program that best suits their schedule, needs, and available resources.

This work was supported by funding from the Centers for Disease Control and Prevention, contract no. HHSD2002013M53964B. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. The authors received no external financial support for the research, authorship, or publication of this article. The authors declared no potential conflicts of interest with respect to the research, authorship, or publication of this article. No copyrighted material, surveys, instruments, or tools were used in this research.

Corresponding author: Wendi Rotunda, PhD, 3040 East Cornwallis Road, Durham North Carolina 27709 ( [email protected] ).

Author Affiliations: 1 RTI International, Research Triangle Park, North Carolina. 2 Centers for Disease Control and Prevention, Atlanta, Georgia. 3 CyberData Technologies, Herndon, Virginia.

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a This search strategy was initially developed as part of a broader systematic review.

Abbreviations: I, intervention; C, comparator; BMI, body mass index; CVD, cardiovascular disease; NIH, National Institutes of Health; UK, United Kingdom. a Study was determined to be low quality because of high participant attrition (>20%).

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Search strategy, data extraction, risk of bias, data synthesis and analysis, medications, youth-directed psychosocial treatments, parent support, school interventions, cognitive training, neurofeedback, nutrition and supplements, complementary, alternative, or integrative medicine, combined medication and behavioral treatments, moderation of treatment response, long-term outcomes, clinical implications, strengths and limitations, future research needs, acknowledgments, treatments for adhd in children and adolescents: a systematic review.

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Bradley S. Peterson , Joey Trampush , Margaret Maglione , Maria Bolshakova , Mary Rozelle , Jeremy Miles , Sheila Pakdaman , Morah Brown , Sachi Yagyu , Aneesa Motala , Susanne Hempel; Treatments for ADHD in Children and Adolescents: A Systematic Review. Pediatrics April 2024; 153 (4): e2024065787. 10.1542/peds.2024-065787

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Effective treatment of attention-deficit/hyperactivity disorder (ADHD) is essential to improving youth outcomes.

This systematic review provides an overview of the available treatment options.

We identified controlled treatment evaluations in 12 databases published from 1980 to June 2023; treatments were not restricted by intervention content.

Studies in children and adolescents with clinically diagnosed ADHD, reporting patient health and psychosocial outcomes, were eligible. Publications were screened by trained reviewers, supported by machine learning.

Data were abstracted and critically appraised by 1 reviewer and checked by a methodologist. Data were pooled using random-effects models. Strength of evidence and applicability assessments followed Evidence-based Practice Center standards.

In total, 312 studies reported in 540 publications were included. We grouped evidence for medication, psychosocial interventions, parent support, nutrition and supplements, neurofeedback, neurostimulation, physical exercise, complementary medicine, school interventions, and provider approaches. Several treatments improved ADHD symptoms. Medications had the strongest evidence base for improving outcomes, including disruptive behaviors and broadband measures, but were associated with adverse events.

We found limited evidence of studies comparing alternative treatments directly and indirect analyses identified few systematic differences across stimulants and nonstimulants. Identified combination of medication with youth-directed psychosocial interventions did not systematically produce better results than monotherapy, though few combinations have been evaluated.

A growing number of treatments are available that improve ADHD symptoms and other outcomes, in particular for school-aged youth. Medication therapies remain important treatment options but are associated with adverse events.

Attention-deficit/hyperactivity disorder (ADHD) is a common mental health problem in youth, with a prevalence of ∼5.3%. 1 , 2   Youth with ADHD are prone to future risk-taking problems, including substance abuse, motor vehicle accidents, unprotected sex, criminal behavior, and suicide attempts. 3   Although stimulant medications are currently the mainstay of treatment of school-age youth with ADHD, other treatments have been developed for ADHD, including cognitive training, neurofeedback, neuromodulation, and dietary and nutritional interventions. 4   – 7  

This systematic review summarizes evidence for treatments of ADHD in children and adolescents. The evidence review extends back to 1980, when contemporary diagnostic criteria for ADHD and long-acting stimulants were first introduced. Furthermore, we did not restrict to a set of prespecified known interventions for ADHD, and instead explored the range of available treatment options for children and adolescents, including novel treatments. Medication evaluations had to adhere to a randomized controlled trial (RCT) design, all other treatments could be evaluated in RCTs or nonrandomized controlled studies that are more common in the psychological literature, as long as the study reported on a concurrent comparator. Outcomes were selected with input from experts and stakeholders and were not restricted to ADHD symptoms. To our knowledge, no previous review for ADHD treatments has been as comprehensive in the range of interventions, clinical and psychosocial outcomes, participant ages, and publication years.

The review aims were developed in consultation with the Agency for Healthcare Research and Quality (AHRQ), the Patient-Centered Outcomes Research Institute, the topic nominator American Academy of Pediatrics (AAP), key informants, a technical expert panel (TEP), and public input. The TEP reviewed the protocol and advised on key outcomes. Subgroup analyses and key outcomes were prespecified. The review is registered in PROSPERO (#CRD42022312656) and the protocol is available on the AHRQ Web site as part of a larger evidence report on ADHD. The systematic review followed Methods of the (AHRQ) Evidence-based Practice Center Program. 8  

Population: Children or adolescents with a clinical diagnosis of ADHD, age <18 years

Interventions: Any ADHD treatment, alone or in combination, and ≥4 weeks’ treatment

Comparators: No treatment, waitlist, placebo, passive comparators, or active comparators

Outcomes: Patient health and psychosocial outcomes

Setting: Any

Study designs: RCTs for medication; RCTs, controlled clinical trials without random assignment, or cohort studies comparing 1 or more treatment groups for nondrug treatments. Studies either had to be large or demonstrate that they could detect effects as a standalone study (operationalized as ≥100 participants or a power calculation)

Other limiters: English-language (to ensure transparency for a US guideline), published from 1980

We searched the databases PubMed, Embase, PsycINFO, ERIC, and ClinicalTrials.gov. We identified reviews for reference-mining through PubMed, Cochrane Database of Systematic Reviews, Campbell Collaboration, What Works in Education, PROSPERO, ECRI Guidelines Trust, G-I-N, and ClinicalKey. The search underwent peer review; the full strategy is in the Online Appendix. All citations were reviewed by trained literature reviewers supported by machine learning to ensure no studies were inadvertently missed. Two independent reviewers assessed full-text studies for eligibility. Publications reporting on the same participants were consolidated into 1 record so that no study entered the analyses more than once. The TEP reviewed studies to ensure all were captured.

The data abstraction form included extensive guidance to aid reproducibility and standardization in recording study details, outcomes, 9   – 12   study quality, and applicability. One reviewer abstracted data, and a methodologist checked its accuracy and completeness. Data are publicly available in the Systematic Review Data Repository.

We assessed 6 domains 13   : Selection, performance, attrition, detection, reporting, and study-specific biases ( Supplemental Figs 6 and 7 ).

We organized analyses by treatment and comparison type. We grouped treatments according to intervention content and target (eg, youth or parents). The intervention taxonomy differentiated medication, psychosocial interventions, parent support, nutrition and supplements, neurofeedback, neurostimulation, physical exercise, complementary medicine, school interventions, and provider approaches. We differentiated effects versus passive control groups (eg, placebo) and comparative effects (ie, comparing to an alternative treatment). The following outcomes were selected as key outcomes: (1) ADHD symptoms (eg, ADHD Rating Scale 14 , 15   ), (2) disruptive behavior (eg, conduct problems), (3) broadband measures (eg, Clinical Global Impression 16   ), (4) functional impairment (eg, Weiss Functional Impairment Rating Scale 17 , 18   ), (5) academic performance (eg, grade point average), (6) appetite suppression, and (7) number of participants reporting adverse events.

Studies reported on a large range of outcome measures as documented in the evidence table in the Online Appendix. To facilitate comparisons across studies, we converted outcomes to scale-independent standardized mean differences (SMDs) for continuous symptom outcome variables and relative risks (RRs) for categorical reports, presenting summary estimates and 95% confidence intervals (CIs) for all analyses. We used random-effects models performed in R with Metafor_v4.2-0 for statistical pooling, correcting for small numbers of studies when necessary, to synthesize available evidence. 19   We conducted sensitivity analyses for all analyses that included studies without random assignment. We also compared treatment effectiveness indirectly across studies in meta-regressions that added potential, prespecified effect modifiers to the meta-analytic model. In particular, we assessed whether ADHD presentation or cooccurring disorders modified intervention effects. We tested for heterogeneity using graphical displays, documented I 2 statistics (values >50% are highlighted in the text), and explored sources of heterogeneity in subgroup and sensitivity analyses. 20  

We assessed publication bias with Begg and Egger tests 21 , 22   and used the trim-and-fill methods for alternative estimates where necessary. 23   Applicability of findings to real-world clinical practices in typical US settings was assessed qualitatively using AHRQ’s Methods Guide. An overall strength of evidence (SoE) assessment communicating our confidence in each finding was determined initially by 1 researcher with experience in use of specified standardized criteria 24   ( Supplemental Information ), then discussed with the study team. We downgraded SoE for study limitations, imprecision, inconsistency, and reporting bias, and we differentiated high, moderate, low, and insufficient SoE.

We screened 23 139 citations and retrieved 7534 publications as full text against the eligibility criteria. In total, 312 treatment studies, reported in 540 publications (see list of included studies in the Online Appendix), met eligibility criteria ( Fig 1 ).

Literature flow diagram.

Although studies from 1980 were eligible, the earliest study meeting all eligibility criteria was from 1995. All included studies are documented in the evidence table in the Supplemental Information . The following highlights key findings. Results for intervention groups and individual studies, subgroup and sensitivity analyses, characteristics of participants and interventions contributing to the analyses, and considerations that determined the SoE for results are documented in the Online Appendix.

As a class, traditional stimulants (methylphenidate, amphetamines) significantly improved ADHD symptom severity (SMD, −0.88; CI, −1.13 to −0.63; studies = 12; n = 1620) and broadband measures (RR, 0.38; CI, 0.30–0.48; studies = 12; n = 1582) (both high SoE), but not functional impairment (SMD, 1.00; CI, −0.25 to 2.26; studies = 4; n = 540) ( Fig 2 , Supplemental Fig 8 , Supplemental Table 1 ). Methylphenidate formulations significantly improved ADHD symptoms (SMD, −0.68; CI, −0.91 to −0.46; studies = 7; n = 863) ( Fig 2 , Supplemental Table 1 ) and broadband measures (SMD, 0.66; CI, 0.04–1.28; studies = 2; n = 302). Only 1 study assessed academic performance, reporting large improvements compared with a control group (SMD, −1.37; CI, −1.72 to −1.03; n = 156) ( Supplemental Fig 9 ). 25   Methylphenidate statistically significantly suppressed appetite (RR, 2.80; CI, 1.47–5.32; studies = 8; n = 1110) ( Fig 3 ), and more patients reported adverse events (RR, 1.32; CI, 1.25–1.40; studies = 6; n = 945). Amphetamine formulations significantly improved ADHD symptoms (SMD, −1.16; CI, −1.64 to −0.67; studies = 5; n = 757) ( Fig 2 , Supplemental Table 1 ) but not broadband measures (SMD, 0.68; CI, −0.72 to 2.08; studies = 3; n = 561) ( Supplemental Fig 9 ). Amphetamines significantly suppressed appetite (RR, 7.08; CI, 2.72–18.42; studies = 8; n = 1229) ( Fig 3 ), and more patients reported adverse events (RR, 1.41; CI, 1.25–1.58; studies = 8; n = 1151). Modafinil (US Food and Drug Administration [FDA]-approved to treat narcolepsy and sleep apnea but not ADHD) in each individual study significantly improved ADHD symptoms, but aggregated estimates were nonsignificant (SMD, −0.76; CI, −1.75 to 0.23; studies = 4; n = 667) ( Fig 2 , Supplemental Table 1 ) because of high heterogeneity (I 2 = 91%). It did not improve broadband measures (RR, 0.49; CI, −0.12 to 2.07; studies = 3; n = 539) ( Supplemental Fig 9 ), and it significantly suppressed appetite (RR, 4.44; CI, 2.27–8.69; studies = 5; n = 780) ( Fig 3 ).

Medication effects on ADHD symptom severity. S-AMPH-LDX, lisdexamfetamine; S-AMPH-MAS, mixed amphetamines salts; S-MPH-DEX, dexmethylphenidate; S-MPH-ER, extended-release methylphenidate; S-MPH-IR, immediate release methylphenidate; S-MPH-OROS, osmotic-release oral system methylphenidate; S-MPH-TP, dermal patch methylphenidate; NS-NRI-ATX, atomoxetine; NS-NRI-VLX, viloxazine; NS-ALA-CLON, clonidine; NS-ALA-GXR, guanfacine extended-release.

Medication effects on appetite suppression. Abbreviations as in legend for Fig 2 .

As a class, nonstimulants significantly improved ADHD symptoms (SMD, −0.52; CI, −0.59 to −0.46; studies = 37; n = 6065; high SoE) ( Fig 2 , Supplemental Table 1 ), broadband measures (RR, 0.66; CI, 0.58–0.76; studies = 12; n = 2312) ( Supplemental Fig 8 ), and disruptive behaviors (SMD, 0.66; CI, 0.22–1.10; studies = 4; n = 523), but not functional impairment (SMD, 0.20; CI, −0.05 to 0.44; studies = 6; n = 1163). Norepinephrine reuptake inhibitors (NRI) improved ADHD symptoms (SMD, −0.55; CI, −0.62 to −0.47; studies=28; n = 4493) ( Fig 2 , Supplemental Table 1 ) but suppressed appetite (RR, 3.23; CI, 2.40–4.34; studies = 27; n = 4176) ( Fig 3 ), and more patients reported adverse events (RR, 1.31; CI, 1.18–1.46; studies = 15; n = 2600). Alpha-agonists (guanfacine and clonidine) improved ADHD symptoms (SMD, −0.52; CI, −0.67 to −0.37; studies = 11; n = 1885) ( Fig 2 , Supplemental Table 1 ), without (guanfacine) significantly suppressing appetite (RR, 1.49; CI, 0.94–2.37; studies = 4; n = 919) ( Fig 3 ), but more patients reported adverse events (RR, 1.21; CI, 1.11–1.31; studies = 14, n = 2544).

One study compared amphetamine versus methylphenidate, head-to-head, finding more improvement in ADHD symptoms (SMD, −0.46; CI, −0.73 to −0.19; n = 222) and broadband measures (SMD, 0.29; CI, 0.02–0.56; n = 211), but not functional impairment (SMD, 0.16; CI, −0.11 to 0.43; n = 211), 26   with lisdexamfetamine (an amphetamine) than osmotic-release oral system methylphenidate. No difference was found in appetite suppression (RR, 1.01; CI, 0.72–1.42; studies = 2, n = 414) ( Fig 3 ) or adverse events (RR, 1.11; CI, 0.93–1.33; study = 1, n = 222). Indirect comparisons yielded significantly larger effects for amphetamine than methylphenidate in improving ADHD symptoms ( P = .02) but not broadband measures ( P = .97) or functional impairment ( P = .68). Stimulants did not differ in appetite suppression ( P = .08) or adverse events ( P = .35).

One study provided information on NRI versus alpha-agonists by directly comparing an alpha-agonist (guanfacine) with an NRI (atomoxetine), 27   finding significantly greater improvement in ADHD symptoms with guanfacine (SMD, −0.47; CI, −0.73 to −0.2; n = 226) but not a broadband measure (RR, 0.84; CI, 0.68–1.04; n = 226). It reported less appetite suppression for guanfacine (RR, 0.48; CI, 0.27–0.83; n = 226) but no difference in adverse events (RR, 1.14; CI, 0.97–1.34; n = 226). Indirect comparisons did not indicate significantly different effect sizes for ADHD symptoms ( P = .90), disruptive behaviors ( P = .31), broadband measures ( P = .41), functional impairment ( P = .46), or adverse events ( P = .06), but suggested NRIs more often suppressed appetite compared with guanfacine ( P = .01).

Studies directly comparing nonstimulants versus stimulants (all were the NRI atomoxetine and stimulants methylphenidate in all but 1) tended to favor stimulants but did not yield significance for ADHD symptom severity (SMD, 0.23; CI, −0.03 to 0.49; studies = 7; n = 1611) ( Fig 2 ). Atomoxetine slightly but statistically significantly produced greater improvements in disruptive behaviors (SMD, −0.08; CI, −0.14 to −0.03; studies = 4; n = 608) ( Supplemental Fig 10 ) but not broadband measures (SMD, −0.16; CI, −0.36 to 0.04; studies = 4; n = 1080) ( Supplemental Fig 9 ). They did not differ significantly in appetite suppression (RR, 0.82; CI, 0.53–1.26; studies = 8; n = 1463) ( Fig 3 ) or number with adverse events (RR, 1.11; CI, 0.90–1.37; studies = 4; n = 756). Indirect comparisons indicated significant differences favoring stimulants over nonstimulants in improving ADHD symptom severity ( P < .0001), broadband measures ( P = .0002), and functional impairment ( P = .04), but not appetite suppression ( P = .31) or number with adverse events ( P = .12).

Several studies assessed whether adding nonstimulant to stimulant medication (all were alpha-agonists added to different stimulants) improved outcomes compared with stimulant medication alone, yielding a small but significant additional improvement in ADHD symptoms (SMD, −0.36; CI, −0.52 to −0.19; studies = 5; n = 724) ( Fig 4 ).

Combination treatment. CLON, clonidine, GXR guanfacine.

We identified 32 studies evaluating psychosocial, psychological, or behavioral interventions targeting ADHD youth, either alone or combined with components for parents and teachers. Interventions were highly diverse, and most were complex with multiple components (see supplemental results in the Online Appendix). They significantly improved ADHD symptoms (SMD, −0.35; CI, −0.51 to −0.19; studies = 14; n = 1686; moderate SoE) ( Fig 4 ), even when restricting to RCTs only (SMD, −0.36; CI, −0.53 to −0.19; removing high-risk-of-bias studies left 7 with similar effects SMD, −0.38; CI, −0.69 to −0.07), with minimal heterogeneity (I 2 = 52%); but not disruptive behaviors (SMD, −0.18; CI, −0.48 to 0.12; studies = 8; n = 947) or academic performance (SMD, −0.07; CI, −0.49 to 0.62; studies = 3; n = 459) ( Supplemental Fig 11 ).

We identified 19 studies primarily targeting parents of youth aged 3 to 18 years, though only 3 included teenagers. Interventions were highly diverse (see Online Appendix), but significantly improved ADHD symptoms (SMD, −0.31; CI, −0.57 to −0.05; studies = 11; n = 1078; low SoE) ( Fig 4 ), even when restricting to RCTs only (SMD, −0.35; CI, −0.61 to −0.09; removing high-risk-of-bias studies yielded the same point estimate, but CIs were wider, and the effect was nonsignificant SMD, −0.31; CI, −0.76 to 0.14). There was some evidence of publication bias (Begg P = .16; Egger P = .02), but the trim and fill method to correct it found a similar effect (SMD, −0.43; CI, −0.63 to −0.22). Interventions improved broadband scores (SMD, 0.41; CI, 0.23–0.58; studies = 7; n = 613) and disruptive behaviors (SMD, −0.52; CI, −0.85 to −0.18; studies = 4; n = 357) but not functional impairment (SMD, 0.35; CI, −0.69 to 1.39; studies = 3; n = 252) (all low SoE) ( Supplemental Fig 12 ).

We identified 10 studies, mostly for elementary or middle schools (see Online Appendix). Interventions did not significantly improve ADHD symptoms (SMD, −0.50; CI, −1.05 to 0.06; studies = 5; n = 822; moderate SoE) ( Fig 4 ), but there was evidence of heterogeneity (I 2 = 87%). Although most studies reported improved academic performance, this was not statistically significant across studies (SMD, −0.19; CI, −0.48 to 0.09; studies = 5; n = 854) ( Supplemental Fig 13 ).

We identified 22 studies, for youth aged 6 to 17 years without intellectual disability (see Online Appendix). Cognitive training did improve ADHD symptoms (SMD, −0.37; CI, −0.65 to −0.06; studies = 12; n = 655; low SoE) ( Fig 4 ), with some heterogeneity (I 2 = 65%), but not functional impairment (SMD, 0.41; CI, −0.24 to 1.06; studies = 5; n = 387) ( Supplemental Fig 14 ) or disruptive behaviors (SMD, −0.29; CI, −0.84 to 0.27; studies [all RCTs] = 5; n = 337). It improved broadband measures (SMD, 0.50; CI, 0.12–0.88; studies = 6; n = 344; RCTs only: SMD, 0.43; CI, −0.06 to 0.93) (both low SoE). It did not increase adverse events (RR, 3.30; CI, 0.03–431.32; studies = 2; n = 402).

We identified 21 studies: Two-thirds involved θ/β EEG marker modulation, and one-third modulation of slow cortical potentials (see Online Appendix). Neurofeedback significantly improved ADHD symptoms (SMD, −0.44; CI, −0.65 to −0.22; studies = 12; n = 945; low SoE) ( Fig 4 ), with little heterogeneity (I 2 = 33%); restricting to the 10 RCTs yielded the same point estimate, also statistically significant (SMD, −0.44; CI, −0.71 to −0.16). Neurofeedback did not systematically improve disruptive behaviors (SMD, −0.33; CI, −1.33 to 0.66; studies = 4; n = 372), or functional impairment (SMD, 0.21; CI, −0.14 to 0.55; studies = 3; n = 332) ( Supplemental Fig 15 ).

We identified 39 studies with highly diverse nutrition interventions (see Online Appendix), including omega-3 (studies = 13), vitamins (studies = 3), or diets (studies = 3), and several evaluated supplements as augmentation to stimulants. Most were placebo-controlled. Across studies, interventions improved ADHD symptoms (SMD, −0.39; CI, −0.67 to −0.12; studies = 23; n = 2357) ( Fig 4 ), even when restricting to RCTs (SMD, −0.32; CI, −0.55 to −0.08), with high heterogeneity (I 2 = 89%) but no publication bias. The group of nutritional approaches also improved disruptive behaviors (SMD, −0.28; CI, −0.37 to −0.18; studies [all RCTs] = 5; n = 360) ( Supplemental Fig 16 , low SoE), without increasing the number reporting adverse events (RR, 0.77; CI, 0.47–1.27; studies = 8; n = 735). However, we did not identify any specific supplements that consistently improved outcomes, including omega-3 (eg, ADHD symptoms: SMD, −0.11; CI, −0.45, 0.24; studies = 7; n = 719; broadband measures: SMD, 0.04; CI, −0.24 to 0.32; studies = 7; n = 755, low SoE).

We identified 6 studies assessing acupuncture, homeopathy, and hippotherapy. They did not individually or as a group significantly improve ADHD symptoms (SMD, −0.15; CI, −1.84 to 1.53; studies = 3; n = 313) ( Fig 4 ) or improve other outcomes across studies (eg, broadband measures: SMD, 0.03; CI, −3.66 to 3.73; studies = 2; n = 218) ( Supplemental Fig 17 ).

Eleven identified studies evaluated a combination of medication- and youth-directed psychosocial treatments. Most allowed children to have common cooccurring conditions, but intellectual disability and severe neurodevelopmental conditions were exclusionary. Medication treatments were stimulant or atomoxetine. Psychosocial treatments included multimodal psychosocial treatment, cognitive behavioral therapy, solution-focused therapy, behavioral therapy, and a humanistic intervention. Studies mostly compared combinations of medication and psychosocial treatment to medication alone, rather than no treatment or placebo. Combined therapy did not statistically significantly improve ADHD symptoms across studies (SMD, −0.36; CI, −0.73 to 0.01; studies = 7; n = 841; low SoE; only 2 individual studies reported statistically significant effects) ( Fig 5 ) or broadband measures (SMD, 0.42; CI, −0.72 to 1.56; studies = 3; n = 171), but there was indication of heterogeneity (I 2 = 71% and 62%, respectively).

Nonmedication intervention effects on ADHD symptom severity.

We found little evidence that either ADHD presentation (inattentive, hyperactive, combined-type) or cooccurring psychiatric disorders modified treatment effects on any ADHD outcome, but few studies addressed this question systematically (see Online Appendix).

Only a very small number of studies (33 of 312) reported on outcomes at or beyond 12 months of follow-up (see Online Appendix). Many did not report on key outcomes of this review. Studies evaluating combined psychosocial and medication interventions, such as the multimodal treatment of ADHD study, 28   did not find sustained effects beyond 12 months. Analyses for medication, psychosocial, neurofeedback, parent support, school intervention, and provider-focused interventions did not find sustained effects for more than a single study reporting on the same outcome. No complementary medicine, neurostimulation, physical exercise, or cognitive training studies reported long-term outcomes.

We identified a large body of evidence contributing to knowledge of ADHD treatments. A substantial number of treatments have been evaluated in strong study designs that provide evidence statements regarding the effects of the treatments on children and adolescents with ADHD. The body of evidence shows that numerous intervention classes significantly improve ADHD symptom severity. This includes large but variable effects for amphetamines, moderate-sized effects for methylphenidate, NRIs, and alpha-agonists, and small effects for youth-directed psychosocial treatment, parent support, neurofeedback, and cognitive training. The SoE for effects on ADHD symptoms was high across FDA-approved medications (methylphenidate, amphetamines, NRIs, alpha-agonists); moderate for psychosocial interventions; and low for parent support, neurofeedback, and nutritional interventions. Augmentation of stimulant medication with non-stimulants produced small but significant additional improvement in ADHD symptoms over stimulant medication alone (low SoE).

We also summarized evidence for other outcomes beyond specific ADHD symptoms and found that broadband measures (ie, global clinical measures not restricted to assessing specific symptoms and documenting overall psychosocial adjustment), methylphenidate (low SoE), nonstimulant medications (moderate SoE), and cognitive training (low SoE) yielded significant, medium-sized effects, and parent support small effects (moderate SoE). For disruptive behaviors, nonstimulant medications (high SoE) and parent support (low SoE) produced significant improvement with medium effect. No treatment modality significantly improved functional impairment or academic performance, though the latter was rarely assessed as a treatment outcome.

The enormous variability in treatment components and delivery of youth-directed psychotherapies, parent support, neurofeedback, and nutrition and supplement therapies, and in ADHD outcomes they have targeted, complicates the synthesis and meta-analysis of their effects compared with the much more uniform interventions, delivery, and outcome assessments for medication therapies. Moreover, most psychosocial and parent support studies compared an active treatment against wait list controls or treatment as usual, which did not control well for the effects of parent or therapist attention or other nonspecific effects of therapy, and they have rarely been able to blind adequately either participants or study assessors to treatment assignment. 29 , 30   These design limitations weaken the SoE for these interventions.

The large number of studies, combined with their medium-to-large effect sizes, indicate collectively and with high SoE that FDA-approved medications improve ADHD symptom severity, broadband measures, functional impairment, and disruptive behaviors. Indirect comparison showed larger effect sizes for stimulants than for nonstimulants in improving ADHD symptoms and functional impairment. Results for amphetamines and methylphenidate varied, and we did not identify head-to-head comparisons of NRIs versus alpha-agonists that met eligibility criteria. Despite compelling evidence for their effectiveness, stimulants and nonstimulants produced more adverse events than did other interventions, with a high SoE. Stimulants and nonstimulant NRIs produced significantly more appetite suppression than placebo, with similar effect sizes for methylphenidate, amphetamine, and NRI, and much larger effects for modafinil. Nonstimulant alpha-agonists (specifically, guanfacine) did not suppress appetite. Rates of other adverse events were similar between NRIs and alpha-agonists.

Perhaps contrary to common belief, we found no evidence that youth-directed psychosocial and medication interventions are systematically better in improving ADHD outcomes when delivered as combination treatments 31   – 33   ; both were effective as monotherapies, but the combination did not signal additional statistically significant benefits (low SoE). However, it should be noted that few psychosocial and medication intervention combinations have been studied to date. We also found that treatment outcomes did not vary with ADHD presentation or the presence of cooccurring psychiatric disorders, but indirect analyses are limited in detecting these effect modifiers, and more research is needed. Furthermore, although children of all ages were eligible for inclusion in the review, we note that very few studies assessed treatments (especially medications) in children <6 years of age; evidence is primarily available for school-age children and adolescents. Finally, despite the research volume, we still know little about long-term effects of ADHD treatments. The limited available body of evidence suggests that most interventions, including combined medication and psychological treatment, yield few significant long-term improvements for most ADHD outcomes.

This review provides compelling evidence that numerous, diverse treatments are available and helpful for the treatment of ADHD. These include stimulant and nonstimulant medications, youth-targeted psychosocial treatments, parent support, neurofeedback, and cognitive training, though nonmedication interventions appear to have considerably weaker effects than medications on ADHD symptoms. Nonetheless, the body of evidence provides youth with ADHD, their parents, and health care providers with options.

The paucity of head-to-head studies comparing treatments precludes research-based recommendations regarding which is likely to be most helpful and which should be tried first, and decisions need to be based on clinical considerations and patient preferences. Stimulant and nonstimulant NRI medications, separately and in head-to-head comparisons, have shown similar effectiveness and rates of side effects, including appetite suppression, across identified studies. The moderate effect sizes for nonstimulant alpha-agonists, their low rate of appetite suppression, and their evidence for effectiveness in augmenting the effects of stimulant medications in reducing ADHD symptom severity provides additional treatment options. Furthermore, we found low SoE that neurofeedback and cognitive training improve ADHD symptoms. We also found that nutritional supplements and dietary interventions improve ADHD symptoms and disruptive behaviors. The SoE for nutritional interventions, however, is still low, and despite the research volume, we did not identify systematic benefits for specific supplements.

Clinical guidelines currently advise starting treatment of youth >6 years of age with FDA-approved medications, 33   which the findings of this review support. Furthermore, FDA-approved medications have been shown to significantly improve broadband measures, and nonstimulant medications have been shown to improve disruptive behaviors, suggesting their clinical benefits extend beyond improving only ADHD symptoms. Clinical guidelines for preschool children advise parent training and/or classroom behavioral interventions as the first line of treatment, if available. These recommendations remain supported by the present review, given the paucity of studies in preschool children in general, and because many existing studies, in particular medication and youth-directed psychosocial interventions, do not include young children. 31   – 33  

This review incorporated publications dating from 1980, assessing diverse intervention targets (youth, parent, school) and ADHD outcomes across numerous functional domains. Limitations in its scope derive from eligibility criteria. Requiring treatment of 4 weeks ensured that interventions were intended as patient treatment rather than proof of concept experiments, but it also excluded some early studies contributing to the field and other brief but intense psychosocial interventions. Requiring studies to be sufficiently large to detect effects excluded smaller studies that contribute to the evidence base. We explicitly did not restrict to RCTs (ie, a traditional medical study design), but instead identified all studies with concurrent comparators so as not to bias against psychosocial research; nonetheless, the large majority of identified studies were RCTs. Our review aimed to provide an overview of the diverse treatment options and we abstracted findings regardless of the suitability of the study results for meta-analysis. Although many ADHD treatments are very different in nature and the clinical decision for 1 treatment approach over another is likely not made primarily on effect size estimates, future research could use the identified study pool and systematically analyze comparative effectiveness of functionally interchangeable treatments in a network meta-analysis, building on previous work on medication options. 34  

Future studies of psychosocial, parent, school-based, neurofeedback, and nutritional treatments should employ more uniform interventions and study designs that provide a higher SoE for effectiveness, including active attention comparators and effective blinding of outcome assessments. Higher-quality studies are needed for exercise and neuromodulation interventions. More trials are needed that compare alternative interventions head-to-head or compare combination treatments with monotherapy. Clinical trials should assess patient-centered outcomes other than ADHD symptoms, including functional impairment and academic performance. Much more research is needed to assess long-term treatment effectiveness, compliance, and safety, including in preschool youth. Studies should assess patient characteristics as modifiers of treatment effects, to identify which treatments are most effective for which patients. To aid discovery and confirmation of these modifiers, studies should make publicly available all individual-level demographic, clinical, treatment, and outcome data.

We thank the following individuals providing expertise and helpful comments that contributed to the systematic review: Esther Lee, Becky Nguyen, Cynthia Ramirez, Erin Tokutomi, Ben Coughli, Jennifer Rivera, Coleman Schaefer, Cindy Pham, Jerusalem Belay, Anne Onyekwuluje, Mario Gastelum, Karin Celosse, Samantha Fleck, Janice Kang, and Sreya Molakalaplli for help with data acquisition. We thank Kymika Okechukwu, Lauren Pilcher, Joanna King, and Robyn Wheatley from the American Academy of Pediatrics; Jennie Dalton and Paula Eguino Medina from the Patient-Centered Outcomes Research Institute; Christine Chang and Kim Wittenberg from AHRQ; and Mary Butler from the Minnesota Evidence-based Practice Center. We thank Glendy Burnett, Eugenia Chan, MD, MPH; Matthew J. Gormley, PhD; Laurence Greenhill, MD; Joseph Hagan, Jr, MD; Cecil Reynolds, PhD; Le’Ann Solmonson, PhD, LPC-S, CSC; and Peter Ziemkowski, MD, FAAFP; who served as key informants. We thank Angelika Claussen, PhD; Alysa Doyle, PhD; Tiffany Farchione, MD; Matthew J. Gormley, PhD; Laurence Greenhill, MD; Jeffrey M. Halperin, PhD; Marisa Perez-Martin, MS, LMFT; Russell Schachar, MD; Le’Ann Solmonson, PhD, LPC-S, CSC; and James Swanson, PhD; who served as a technical expert panel. Finally, we thank Joel Nigg, PhD; and Peter S. Jensen, MD; for their peer review of the data.

Drs Peterson and Hempel conceptualized and designed the study, collected data, conducted the analyses, drafted the initial manuscript, and critically reviewed and revised the manuscript; Dr Trampush conducted the critical appraisal; Drs Bolshakova and Pakdaman, and Ms Rozelle, Ms Maglione, and Ms Brown screened citations and abstracted the data; Dr Miles conducted the analyses; Ms Yagyu designed and executed the search strategy; Ms Motala served as data manager; and all authors provided critical input for the manuscript, approved the final manuscript as submitted, and agree to be accountable for all aspects of the work.

This study is registered at PROSPERO, #CRD42022312656. Data are available in SRDRPlus.

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2024-065854 .

FUNDING: The work is based on research conducted by the Southern California Evidence-based Practice Center under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract No. 75Q80120D00009). The Patient-Centered Outcomes Research Institute funded the research (Publication No. 2023-SR-03). The findings and conclusions in this manuscript are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views of the AHRQ or the Patient-Centered Outcomes Research Institute, its board of governors or methodology committee. Therefore, no statement in this report should be construed as an official position of the Patient-Centered Outcomes Research Institute, the AHRQ, or the US Department of Health and Human Services.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no conflicts of interest relevant to this article to disclose.

attention-deficit/hyperactivity disorder

Agency for Healthcare Research and Quality

US Food and Drug Administration

confidence interval

norepinephrine reuptake inhibitors

randomized controlled trial

relative risk

standardized mean difference

strength of evidence

technical expert panel

Supplementary data

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Nutrition Evidence Systematic Review (NESR) is a team of scientists at the USDA Center for Nutrition Policy and Promotion. NESR specializes in conducting food- and nutrition-related systematic reviews, rapid reviews, and evidence scans to help inform nutrition program and nutrition policy decisions in the Federal government, such as the Dietary Guidelines for Americans .

NESR systematic reviews are gold-standard evidence synthesis projects that answer nutrition questions of public health importance using systematic, transparent, rigorous, and protocol-driven methods to search for, evaluate, synthesize, and grade the strength of the eligible body of evidence.

NESR rapid reviews are evidence synthesis projects that answer a nutrition question of public health importance using streamlined systematic review methods. Methods used to search for, evaluate, synthesize, or assess the evidence may be tailored to conserve resources and produce a timelier product when full systematic review methods are not needed or feasible.

NESR evidence scans are exploratory evidence description projects in which systematic methods are used to search for and describe the volume and characteristics of evidence available on a nutrition question or topic of public health importance. Evidence scans can be either stand-alone projects or the beginning steps of conducting a rapid or systematic review.

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• Dietary Patterns and Risk of Type 2 Diabetes: A Systematic Review Carol Boushey, Jamy Ard, Lydia Bazzano, Steven Heymsfield, Elizabeth Mayer-Davis, Joan Sabaté, Linda Snetselaar, Linda Van Horn, Barbara Schneeman, Laural Kelly English, Marlana Bates, Emily Callahan, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Dietary Patterns and Sarcopenia: A Systematic Review Carol Boushey, Jamy Ard, Lydia Bazzano, Steven Heymsfield, Elizabeth Mayer-Davis, Joan Sabaté, Linda Snetselaar, Linda Van Horn, Barbara Schneeman, Laural Kelly English, Marlana Bates, Emily Callahan, Sudha Venkatramanan, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Dietary Patterns during Lactation and Developmental Milestones, including Neurocognitive Development, in the Child: A Systematic Review Sharon Donovan, Kathryn Dewey, Rachel Novotny, Jamie Stang, Elsie Taveras, Ronald Kleinman, Ramkripa Raghavan, Julie Nevins, Sara Scinto-Madonich, Nancy Terry, Gisela Butera, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Dietary Patterns during Lactation and Human Milk Composition and Quantity: A Systematic Review Sharon Donovan, Kathryn Dewey, Rachel Novotny, Jamie Stang, Elsie Taveras, Ronald Kleinman, Ramkripa Raghavan, Julie Nevins, Sara Scinto-Madonich, Julia H. Kim, Nancy Terry, Gisela Butera, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Dietary Patterns during Lactation and Postpartum Weight Loss: A Systematic Review Sharon Donovan, Kathryn Dewey, Rachel Novotny, Jamie Stang, Elsie Taveras, Ronald Kleinman, Ramkripa Raghavan, Julie Nevins, Sara Scinto-Madonich, Julia H. Kim, Nancy Terry, Gisela Butera, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Dietary Patterns during Pregnancy and Gestational Weight Gain: A Systematic Review Sharon Donovan, Kathryn Dewey, Rachel Novotny, Jamie Stang, Elsie Taveras, Ronald Kleinman, Ramkripa Raghavan, Julie Nevins, Sara Scinto-Madonich, Julia H. Kim, Nancy Terry, Gisela Butera, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• The Duration, Frequency, and Volume of Exclusive Human Milk and/or Infant Formula Consumption and Nutrient Status: A Systematic Review Kathryn Dewey, Lydia Bazzano, Teresa Davis, Sharon Donovan, Elsie Taveras, Ronald Kleinman, Darcy Güngör, Emily Madan, Sudha Venkatramanan, Nancy Terry, Gisela Butera, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• The Duration, Frequency, and Volume of Exclusive Human Milk and/or Infant Formula Consumption and Overweight and Obesity: A Systematic Review Kathryn Dewey, Lydia Bazzano, Teresa Davis, Sharon Donovan, Elsie Taveras, Ronald Kleinman, Darcy Güngör, Emily Madan, Sudha Venkatramanan, Nancy Terry, Gisela Butera, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Folic Acid from Fortified Foods and/or Supplements during Pregnancy and Lactation and Health Outcomes: A Systematic Review Sharon Donovan, Kathryn Dewey, Rachel Novotny, Jamie Stang, Elsie Taveras, Ronald Kleinman, Maureen Spill, Julia H. Kim, Julie Nevins, Ramkripa Raghavan, Sara Scinto-Madonich, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Frequency of Eating and All-Cause Mortality: A Systematic Review Steven Heymsfield, Carol Boushey, Heather Leidy, Richard Mattes, Ronald Kleinman, Emily Callahan, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Frequency of Eating and Cardiovascular Disease: A Systematic Review Steven Heymsfield, Carol Boushey, Heather Leidy, Richard Mattes, Ronald Kleinman, Emily Callahan, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Frequency of Eating and Growth, Size, Body Composition, and Risk of Overweight and Obesity: A Systematic Review: A Systematic Review Steven Heymsfield, Carol Boushey, Heather Leidy, Richard Mattes, Ronald Kleinman, Emily Callahan, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Frequency of Eating and Type 2 Diabetes: A Systematic Review Steven Heymsfield, Carol Boushey, Heather Leidy, Richard Mattes, Ronald Kleinman, Emily Callahan, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Frequency of Eating during Lactation and Postpartum Weight Loss: A Systematic Review Steven Heymsfield, Carol Boushey, Heather Leidy, Richard Mattes, Ronald Kleinman, Emily Callahan, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Frequency of Eating during Pregnancy and Gestational Weight Gain: A Systematic Review Steven Heymsfield, Carol Boushey, Heather Leidy, Richard Mattes, Ronald Kleinman, Emily Callahan, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Iron from Supplements Consumed During Infancy and Toddlerhood and Growth, Size, and Body Composition: A Systematic Review Kathryn Dewey, Lydia Bazzano, Teresa Davis, Sharon Donovan, Elsie Taveras, Ronald Kleinman, Darcy Güngör, Sudha Venkatramanan, Emily Madan, Laural Kelly English, Nancy Terry, Gisela Butera, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Maternal Diet during Pregnancy and Lactation and Risk of Child Food Allergies and Atopic Allergic Diseases: A Systematic Review Sharon Donovan, Kathryn Dewey, Rachel Novotny, Jamie Stang, Elsie Taveras, Ronald Kleinman, Ramkripa Raghavan, Julie Nevins, Sara Scinto-Madonich, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Omega-3 fatty acids from Supplements Consumed before and during Pregnancy and Lactation and Developmental Milestones, Including Neurocognitive Development, in the Child: A Systematic Review Sharon Donovan, Kathryn Dewey, Rachel Novotny, Jamie Stang, Elsie Taveras, Ronald Kleinman, Julie Nevins, Ramkripa Raghavan, Sara Scinto-Madonich, Sudha Venkatramanan, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Seafood Consumption during Childhood and Adolescence and Cardiovascular Disease: A Systematic Review Linda Snetselaar, Regan Bailey, Joan Sabaté, Linda Van Horn, Barbara Schneeman, Julia H. Kim, Joanne Spahn, Charlotte Bahnfleth, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Seafood Consumption during Childhood and Adolescence and Neurocognitive Development: A Systematic Review Linda Snetselaar, Regan Bailey, Joan Sabaté, Linda Van Horn, Barbara Schneeman, Joanne Spahn, Julia H. Kim, Charlotte Bahnfleth, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Seafood Consumption during Pregnancy and Lactation and Neurocognitive Development in the Child: A Systematic Review Linda Snetselaar, Regan Bailey, Joan Sabaté, Linda Van Horn, Barbara Schneeman, Joanne Spahn, Julia H. Kim, Charlotte Bahnfleth, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Types of Dietary Fat and Cardiovascular Disease: A Systematic Review Linda Snetselaar, Regan Bailey, Joan Sabaté, Linda Van Horn, Barbara Schneeman, Charlotte Bahnfleth, Julia H. Kim, Joanne Spahn, Gisela Butera, Nancy Terry, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Vitamin D from Supplements Consumed during Infancy and Toddlerhood and Bone Health: A Systematic Review Kathryn Dewey, Lydia Bazzano, Teresa Davis, Sharon Donovan, Elsie Taveras, Ronald Kleinman, Darcy Güngör, Sudha Venkatramanan, Emily Madan, Laural Kelly English, Nancy Terry, Gisela Butera, and Julie Obbagy. Alexandria (VA): USDA Nutrition Evidence Systematic Review; July 2020.
• Dietary Patterns before and during Pregnancy and Gestational Age at Birth: A Systematic Review Ramkripa Raghavan, Carol Dreibelbis, Brittany James Kingshipp, Yat Ping Wong, Nancy Terry, Barbara Abrams, Anne Bartholomew, Lisa M. Bodnar, Alison Gernand, Kathleen Rasmussen, Anna Maria Siega-Riz, Jamie S. Stang, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Dietary Patterns before and during Pregnancy and Gestational Age- and Sex-Specific Birth Weight: A Systematic Review Ramkripa Raghavan, Carol Dreibelbis, Brittany James Kingshipp, Yat Ping Wong, Nancy Terry, Barbara Abrams, Anne Bartholomew, Lisa M. Bodnar, Alison Gernand, Kathleen Rasmussen, Anna Maria Siega-Riz, Jamie S. Stang, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Dietary Patterns before and during Pregnancy and Risk of Gestational Diabetes Mellitus: A Systematic Review Ramkripa Raghavan, Carol Dreibelbis, Brittany James Kingshipp, Yat Ping Wong, Nancy Terry, Barbara Abrams, Anne Bartholomew, Lisa M. Bodnar, Alison Gernand, Kathleen Rasmussen, Anna Maria Siega-Riz, Jamie S. Stang, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Dietary Patterns before and during Pregnancy and Risk of Hypertensive Disorders of Pregnancy: A Systematic Review Ramkripa Raghavan, Carol Dreibelbis, Brittany James Kingshipp, Yat Ping Wong, Nancy Terry, Barbara Abrams, Anne Bartholomew, Lisa M. Bodnar, Alison Gernand, Kathleen Rasmussen, Anna Maria Siega-Riz, Jamie S. Stang, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Feeding a Higher Intensity, Proportion, or Amount of Human Milk by Bottle Versus By Breast and Food Allergies, Allergic Rhinitis, Atopic Dermatitis, and Asthma: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Feeding a Lower Versus Higher Intensity, Proportion, or Amount of Human Milk to Mixed-Fed Infants and Cardiovascular Disease Outcomes in Offspring: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Feeding a Lower Versus Higher Intensity, Proportion, or Amount of Human Milk to Mixed-Fed Infants and Celiac Disease: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Feeding a Lower Versus Higher Intensity, Proportion, or Amount of Human Milk to Mixed-Fed Infants and Childhood Leukemia: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Feeding a Lower Versus Higher Intensity, Proportion, or Amount of Human Milk to Mixed-Fed Infants and Diabetes Outcomes in Offspring: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Feeding a Lower Versus Higher Intensity, Proportion, or Amount of Human Milk to Mixed-Fed Infants and Food Allergies, Allergic Rhinitis, Atopic Dermatitis, and Asthma: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Feeding a Lower Versus Higher Intensity, Proportion, or Amount of Human Milk to Mixed-Fed Infants and Inflammatory Bowel Disease: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Influence of Maternal Diet on Flavor Transfer to Amniotic Fluid and Breast Milk and Children’s Responses: A Systematic Review Maureen Spill, Emily Callahan, Kirsten Johns, Myra Shapiro, Joanne M. Spahn, Yat Ping Wong, Nancy Terry, Sara Benjamin-Neelon, Leann Birch, Maureen Black, Ronette Briefel, John Cook, Myles Faith, Julie Mennella, Kellie O. Casavale, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Never Versus Ever Feeding Human Milk and Cardiovascular Disease Outcomes in Offspring: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Never Versus Ever Feeding Human Milk and Celiac Disease: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Never Versus Ever Feeding Human Milk and Childhood Leukemia: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Never Versus Ever Feeding Human Milk and Diabetes Outcomes in Offspring: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Never Versus Ever Feeding Human Milk and Food Allergies, Allergic Rhinitis, Atopic Dermatitis, and Asthma: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Never Versus Ever Feeding Human Milk and Inflammatory Bowel Disease: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Parental and Caregiver Feeding Practices and Growth, Size, and Body Composition Outcomes: A Systematic Review Maureen Spill, Emily Callahan, Kirsten Johns, Myra Shapiro, Joanne M. Spahn, Yat Ping Wong, Nancy Terry, Sara Benjamin-Neelon, Leann Birch, Maureen Black, Ronette Briefel, John Cook, Myles Faith, Julie Mennella, Kellie O. Casavale, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Repeated Exposure to Foods and Early Food Acceptance: A Systematic Review Maureen Spill, Emily Callahan, Kirsten Johns, Myra Shapiro, Joanne M. Spahn, Yat Ping Wong, Nancy Terry, Sara Benjamin-Neelon, Leann Birch, Maureen Black, Ronette Briefel, John Cook, Myles Faith, Julie Mennella, Kellie O. Casavale, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Any Human Milk Feeding and Cardiovascular Disease Outcomes in Offspring: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Any Human Milk Feeding and Celiac Disease: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Any Human Milk Feeding and Childhood Leukemia: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Any Human Milk Feeding and Diabetes Outcomes in Offspring: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Any Human Milk Feeding and Food Allergies, Allergic Rhinitis, Atopic Dermatitis, and Asthma: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Any Human Milk Feeding and Inflammatory Bowel Disease: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Exclusive Human Milk Feeding and Cardiovascular Disease Outcomes in Offspring: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Exclusive Human Milk Feeding and Celiac Disease: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Exclusive Human Milk Feeding and Childhood Leukemia: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Exclusive Human Milk Feeding and Diabetes Outcomes in Offspring: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Exclusive Human Milk Feeding and Inflammatory Bowel Disease: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Shorter Versus Longer Durations of Exclusive Human Milk Feeding Prior to the Introduction of Infant Formula and Food Allergies, Allergic Rhinitis, Atopic Dermatitis, and Asthma: A Systematic Review Darcy Güngör, Perrine Nadaud, Carol Dreibelbis, Concetta LaPergola, Nancy Terry, Yat Ping Wong, Steve A. Abrams, Leila Beker, Tova Jacobovits, Kirsi M Järvinen, Laurie A. Nommsen-Rivers, Kimberly O. O’Brien, Emily Oken, Rafael Pérez-Escamilla, Ekhard Ziegler, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Timing of Introduction of Complementary Foods and Beverages and Bone Health: A Systematic Review Julie E. Obbagy, Laural K. English, Tricia L. Psota, Perrine Nadaud, Kirsten Johns, Yat Ping Wong, Nancy Terry, Nancy F. Butte, Kathryn G. Dewey, David M. Fleischer, Mary Kay Fox, Frank R. Greer, Nancy F. Krebs, Kelley S. Scanlon, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Timing of Introduction of Complementary Foods and Beverages and Developmental Milestones: A Systematic Review Laural K. English, Julie E. Obbagy, Yat Ping Wong, Tricia L. Psota, Perrine Nadaud, Kirsten Johns, Nancy Terry, Nancy F. Butte, Kathryn G. Dewey, David M. Fleischer, Mary Kay Fox, Frank R. Greer, Nancy F. Krebs, Kelley S. Scanlon, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Timing of Introduction of Complementary Foods and Beverages and Food Allergy, Atopic Dermatitis/Eczema, Asthma, and Allergic Rhinitis: A Systematic Review Julie E. Obbagy, Laural K. English, Tricia L. Psota, Perrine Nadaud, Kirsten Johns, Yat Ping Wong, Nancy Terry, Nancy F. Butte, Kathryn G. Dewey, David M. Fleischer, Mary Kay Fox, Frank R. Greer, Nancy F. Krebs, Kelley S. Scanlon, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Timing of Introduction of Complementary Foods and Beverages and Growth, Size, and Body Composition: A Systematic Review Laural K. English, Julie E. Obbagy, Yat Ping Wong, Tricia L. Psota, Perrine Nadaud, Kirsten Johns, Nancy Terry, Nancy F. Butte, Kathryn G. Dewey, David M. Fleischer, Mary Kay Fox, Frank R. Greer, Nancy F. Krebs, Kelley S. Scanlon, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Timing of Introduction of Complementary Foods and Beverages and Micronutrient Status: A Systematic Review Julie E. Obbagy, Laural K. English, Tricia L. Psota, Perrine Nadaud, Kirsten Johns, Yat Ping Wong, Nancy Terry, Nancy F. Butte, Kathryn G. Dewey, David M. Fleischer, Mary Kay Fox, Frank R. Greer, Nancy F. Krebs, Kelley S. Scanlon, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Types and Amounts of Complementary Foods and Beverages and Bone Health: A Systematic Review Julie E. Obbagy, Laural K. English, Tricia L. Psota, Perrine Nadaud, Kirsten Johns, Yat Ping Wong, Nancy Terry, Nancy F. Butte, Kathryn G. Dewey, David M. Fleischer, Mary Kay Fox, Frank R. Greer, Nancy F. Krebs, Kelley S. Scanlon, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Types and Amounts of Complementary Foods and Beverages and Developmental Milestones: A Systematic Review Laural K. English, Julie E. Obbagy, Yat Ping Wong, Tricia L. Psota, Perrine Nadaud, Kirsten Johns, Nancy Terry, Nancy F. Butte, Kathryn G. Dewey, David M. Fleischer, Mary Kay Fox, Frank R. Greer, Nancy F. Krebs, Kelley S. Scanlon, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Types and Amounts of Complementary Foods and Beverages and Food Allergy, Atopic Dermatitis/Eczema, Asthma, and Allergic Rhinitis: A Systematic Review Julie E. Obbagy, Laural K. English, Tricia L. Psota, Perrine Nadaud, Kirsten Johns, Yat Ping Wong, Nancy Terry, Nancy F. Butte, Kathryn G. Dewey, David M. Fleischer, Mary Kay Fox, Frank R. Greer, Nancy F. Krebs, Kelley S. Scanlon, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Types and Amounts of Complementary Foods and Beverages and Growth, Size, and Body Composition: A Systematic Review Laural K. English, Julie E. Obbagy, Yat Ping Wong, Tricia L. Psota, Perrine Nadaud, Kirsten Johns, Nancy Terry, Nancy F. Butte, Kathryn G. Dewey, David M. Fleischer, Mary Kay Fox, Frank R. Greer, Nancy F. Krebs, Kelley S. Scanlon, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Types and Amounts of Complementary Foods and Beverages and Micronutrient Status: A Systematic Review Julie E. Obbagy, Laural K. English, Tricia L. Psota, Perrine Nadaud, Kirsten Johns, Yat Ping Wong, Nancy Terry, Nancy F. Butte, Kathryn G. Dewey, David M. Fleischer, Mary Kay Fox, Frank R. Greer, Nancy F. Krebs, Kelley S. Scanlon, Kellie O. Casavale, Joanne M. Spahn, and Eve Stoody. Alexandria (VA): USDA Nutrition Evidence Systematic Review; April 2019.
• Cite this Page USDA Nutrition Evidence Systematic Reviews [Internet]. Alexandria (VA): USDA Nutrition Evidence Systematic Review; 2019-.

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SYSTEMATIC REVIEW article

Game-based learning in early childhood education: a systematic review and meta-analysis.

• Department of Kindergarten, College of Education, Najran University, Najran, Saudi Arabia

Game-based learning has gained popularity in recent years as a tool for enhancing learning outcomes in children. This approach uses games to teach various subjects and skills, promoting engagement, motivation, and fun. In early childhood education, game-based learning has the potential to promote cognitive, social, and emotional development. This systematic review and meta-analysis aim to summarize the existing literature on the effectiveness of game-based learning in early childhood education This systematic review and meta-analysis examine the effectiveness of game-based learning in early childhood education. The results show that game-based learning has a moderate to large effect on cognitive, social, emotional, motivation, and engagement outcomes. The findings suggest that game-based learning can be a promising tool for early childhood educators to promote children’s learning and development. However, further research is needed to address the remaining gaps in the literature. The study’s findings have implications for educators, policymakers, and game developers who aim to promote positive child development and enhance learning outcomes in early childhood education.

1 Introduction

Game-based learning in early childhood education has evolved over time, driven by advancements in technology, educational research, and changing pedagogical approaches. Digital game-based learning refers to the use of digital technology, such as computers or mobile devices, to deliver educational content through interactive games ( Behnamnia et al., 2020 ). Game-based learning, on the other hand, is a broader term that encompasses both digital and non-digital games as tools for educational purposes. In the early years, educational games were primarily non-digital, consisting of board games, puzzles, and manipulatives designed to teach basic concepts and skills ( Pivec, 2007 ). These games often focused on early literacy, numeracy, and problem-solving. With the advent of computers and educational software, digital games emerged as a new medium for learning in the late 20th century. Early educational computer games, such as “Reader Rabbit” and “Math Blaster,” aimed to engage young learners through interactive gameplay while reinforcing educational content. As technology continued to advance, game-based learning expanded beyond standalone software to web-based platforms, mobile apps, and immersive virtual environments ( Shamir et al., 2019 ). The introduction of touchscreen devices, such as tablets and smartphones, made educational games more accessible and interactive for young children. These advancements allowed for greater customization, adaptive learning experiences, and real-time feedback, tailoring the games to meet the individual needs and abilities of young learners.

Researchers and educators recognized the potential of game-based learning to enhance engagement, motivation, and learning outcomes in early childhood education. Studies began to explore the cognitive, social, emotional, and behavioral effects of game-based learning, highlighting its effectiveness in promoting critical thinking, problem-solving, collaboration, creativity, and digital literacy skills ( Park and Park, 2021 ).

In early childhood education, online educational game-based learning has gained popularity as a tool to promote cognitive, social, and emotional development in young children ( Anastasiadis et al., 2018 ). Online educational games are interactive digital games specifically designed to educate and teach children a wide range of skills and concepts. These games utilize engaging and interactive elements to promote learning in areas such as literacy, numeracy, problem-solving, and critical thinking ( Papanastasiou et al., 2022 ). These games are typically played on digital devices such as computers, tablets, and smartphones, and they offer a variety of engaging and interactive learning experiences for young children. Young children are naturally curious and have a strong desire to explore and learn about their environment ( Gurholt and Sanderud, 2016 ). Online educational game-based learning taps into this natural curiosity and provides children with opportunities to engage in meaningful and engaging learning experiences. These games can be tailored to meet the unique needs and abilities of young children, and they can be adapted to suit different learning styles and preferences ( Qian and Clark, 2016 ).

One of the key benefits of online educational game-based learning in early childhood education is its ability to promote cognitive development ( Ferreira et al., 2016 ). Online games can help children develop their problem-solving skills, memory, attention, and processing speed. For example, puzzle games can help children develop their spatial reasoning and problem-solving skills, while memory games can help them improve their memory and concentration ( Suhana, 2017 ).

In addition to promoting cognitive development, online educational game-based learning can also enhance social development in young children. Online games provide children with opportunities to interact with their peers and develop important social skills such as cooperation, communication, and empathy. Children can learn to work together, take turns, and share resources, which are essential skills for building positive relationships and succeeding in life ( Lamrani and Abdelwahed, 2020 ).

Moreover, online educational game-based learning can promote emotional development in young children ( Peterson et al., 2016 ). Online games can help children develop their emotional regulation skills, self-awareness, and self-confidence ( Simion and Bănuț, 2020 ). Games that involve role-playing can help children develop their emotional intelligence and understand different perspectives, while games that require children to take risks and try new things can help them build resilience and confidence ( Huynh et al., 2020 ).

This distinction is further exemplified in studies using online educational game-based learning in early childhood education for is its ability to increase children’s motivation and engagement in learning ( Hwa, 2018 ). Traditional teaching methods can sometimes be dry and one-dimensional, leading to disengagement and boredom in children ( Fotaris et al., 2016 ). Online educational games, on the other hand, provide a fun and interactive way to learn, which can increase children’s motivation and engagement in learning ( Nieto-Escamez and Roldán-Tapia, 2021 ). Children are more likely to be engaged in learning when they are having fun and enjoying the process ( Iten and Petko, 2016 ). Furthermore, online educational game-based learning can be tailored to meet the individual needs and abilities of young children ( Ke, 2014 ). Online games can be adapted to suit different learning styles and preferences, ensuring that all children can benefit from this approach to learning. This is certainly true in the case of games that involve movement and physical activity can be used to promote learning in children who have a kinesthetic learning style, while games that involve visual aids can be used to promote learning in children who have a visual learning style ( Hayati et al., 2017 ).

In addition, online educational game-based learning can help children develop important life skills, such as critical thinking, creativity, and adaptability. Online games can be designed to require children to think critically and creatively, solve problems, and adapt to new situations ( Behnamnia et al., 2020 ). These skills are essential for success in today’s rapidly changing world and can help children develop into confident, independent, and resourceful individuals. Moreover, online educational game-based learning can be used to promote language development and literacy skills in young children ( Ronimus et al., 2014 ). Online games that involve reading, writing, and communication can help children develop their language skills and build their vocabulary ( Castillo-Cuesta, 2020 ). Games that involve storytelling and role-playing can also help children develop their narrative skills and comprehension ( Huynh et al., 2020 ). Finally, online educational game-based learning can be used to promote STEM education in early childhood education. Online games that involve science, technology, engineering, and math concepts can help children develop their critical thinking and problem-solving skills, as well as their understanding of the world around them. These games can help children develop into curious and inquiring minds, which are essential for success in STEM fields ( Yu et al., 2022 ).

Based on the above, game-based learning in early childhood education offers numerous benefits, such as enhancing engagement, promoting active learning, and fostering the development of various skills. However, it is essential to acknowledge and address potential drawbacks or challenges associated with this approach to ensure its effective implementation. One notable challenge is the need for careful game selection. Not all educational games are created equally, and some may lack appropriate content, fail to align with specific learning objectives, or not adequately support the developmental needs of young learners ( Domoff et al., 2019 ). It is crucial to critically evaluate the quality, educational value, and appropriateness of games before incorporating them into early childhood education settings ( Derevensky et al., 2019 ). Another challenge is the limited generalizability of skills acquired through games. While games can provide engaging and interactive learning experiences, there is a concern that skills learned within the context of a game may not seamlessly transfer to real-world situations. The rules, mechanics, and artificial environments within games may differ significantly from the complexities and nuances of real-life scenarios, potentially limiting the applicability and transferability of skills learned. It is important for educators to provide explicit connections and opportunities for children to apply their game-based learning experiences to real-life contexts ( All et al., 2021 ).

Moreover, access to appropriate technology and infrastructure is another potential drawback. Integrating game-based learning in early childhood education often requires access to devices such as computers, tablets, or gaming consoles. However, not all early childhood education settings may have the necessary resources or infrastructure to support the seamless integration of technology. Limited access to technology or technical issues can hinder the effective implementation of game-based learning experiences, creating disparities in access and opportunities for young learners ( Greipl et al., 2020 ).

Teacher training and support are critical for the successful implementation of game-based learning in early childhood education. Educators need to be equipped with the necessary knowledge, skills, and pedagogical approaches to effectively integrate games into the curriculum and facilitate meaningful learning experiences. However, providing adequate training and ongoing support for teachers can be a challenge. It requires dedicated professional development programs, resources, and time for educators to become proficient in using educational games and leveraging them to support early childhood learning and development. Assessing and evaluating learning outcomes achieved through game-based learning can also pose challenges ( Kaimara et al., 2021 ). Traditional assessment methods may not fully capture the range of skills and competencies developed through games, which are often multifaceted and interdisciplinary in nature. Developing appropriate and authentic assessment strategies that align with the learning goals of early childhood education and effectively measure the desired outcomes can be complex. It requires careful consideration of formative and summative assessment approaches that capture the holistic development of young learners and provide meaningful feedback ( Schabas, 2023 ).

Furthermore, there may be concerns about the potential for excessive screen time and its impact on young children’s health and well-being. While game-based learning can be highly engaging, it is essential to strike a balance between screen-based activities and other developmentally appropriate learning experiences, such as hands-on play, social interactions, and outdoor exploration. Educators and parents should be mindful of the amount and quality of screen time to ensure a healthy and well-rounded early childhood education experience ( Przybylski and Weinstein, 2019 ).

Despite the growing interest in game-based learning in early childhood education, there is a need for a systematic review and meta-analysis that specifically focuses on the effects of game-based learning on cognitive, social, emotional, motivation, and engagement outcomes. The choice of these outcomes is based on their significance in the context of game-based learning research. Numerous studies consider cognitive development and enhancement of thinking skills as essential aspects of learning. Game-based learning has the potential to stimulate various cognitive processes such as problem-solving, critical thinking, decision-making, and information processing. Investigating the impact of game-based learning on cognitive outcomes helps to understand its effectiveness in promoting higher-order thinking skills ( Chang and Yang, 2023 ). Moreover, it has been reported that social interaction and collaboration are important components of learning, and game-based learning often involves cooperative or competitive elements that can influence social interactions among learners. Exploring the impact of game-based learning on social outcomes can shed light on how it affects teamwork, communication, and social skills development ( Sun et al., 2022 ). Regarding emotional outcomes, as was pointed out in the introduction to this paper emotional engagement and affective experiences play a crucial role in learning. Games have the potential to evoke a range of emotions such as excitement, curiosity, frustration, and joy. Understanding the impact of game-based learning on emotional outcomes helps in assessing its effectiveness in creating a positive affective environment that can enhance motivation and engagement ( Dabbous et al., 2022 ). Recent research has suggested that examining the impact of game-based learning on motivational outcomes can explore aspects such as intrinsic motivation, self-efficacy, persistence, and enjoyment, which are crucial for effective learning experiences especially for kids in kindergarten ( Yu and Tsuei, 2022 ). Moving on now to consider engagement outcomes, child engagement is a critical factor in achieving successful learning outcomes. Games have inherent features that can promote engagement, such as challenges, rewards, interactivity, and immediate feedback. Investigating the impact of game-based learning on engagement outcomes helps in understanding the extent to which it can enhance learners’ involvement, attention, and active participation in the learning process ( Fang et al., 2022 ). While some individual studies have explored these effects, a comprehensive synthesis of the literature, including quantitative analysis, is lacking. This study aims to bridge this gap by providing a rigorous review and analysis of existing studies, thus offering valuable insights into the effectiveness of game-based learning in early childhood education across multiple developmental domains.

This systematic review and meta-analysis aim to summarize the existing literature on the effectiveness of online game-based learning in early childhood education. Specifically, we will examine the impact of game-based learning on children’s cognitive, social, and emotional development, as well as their motivation and engagement in learning. The primary objective of this study is to investigate the effect of game-based learning on cognitive, social, emotional, motivation, and engagement outcomes in early childhood education. Specifically, the study aims to answer the following questions:

1. What is the effect of game-based learning on cognitive development in early childhood education?

2. What is the effect of game-based learning on social development in early childhood education?

3. What is the effect of game-based learning on emotional development in early childhood education?

4. What is the effect of game-based learning on motivation in early childhood education?

5. What is the effect of game-based learning on engagement in early childhood education?

2 Materials and methods

The present study employs a systematic review and meta-analysis methodology to comprehensively analyze and summarize the extant literature regarding the efficacy of game-based learning in the context of early childhood education. Specifically, the study aims to investigate the effects of game-based learning on various facets of children’s development, including cognitive, social, and emotional domains, as well as their motivation and engagement levels in the learning process.

Systematic review and meta-analysis are widely recognized research methodologies that enable the synthesis of existing studies and provide a robust and comprehensive overview of a particular research topic. By systematically searching, selecting, and critically evaluating relevant empirical studies, the researchers ensure the inclusion of high-quality evidence in the analysis. Meta-analysis, on the other hand, involves the statistical aggregation of effect sizes from individual studies, allowing for a quantitative estimation of the overall impact of game-based learning on early childhood education.

2.1 A systematic review

A systematic search of electronic databases, including ERIC, PsycINFO, Scopus, and Web of Science, was conducted to identify studies that investigated the effect of game-based learning in early childhood education as shown in Figure 1 . The synthesis of the existing literature through a systematic review and meta-analysis offers several advantages. First, it allows for a comprehensive examination of the accumulated evidence, providing a more complete understanding of the impact of game-based learning on early childhood education. Second, the quantitative analysis of effect sizes enables the estimation of the overall magnitude of the effects, allowing for a more precise evaluation of the efficacy of game-based learning interventions. Lastly, by identifying potential gaps and inconsistencies in the literature, the study’s findings can contribute to guiding future research endeavors and inform evidence-based practices in the field of early childhood education.

Figure 1 . Systematic review process ( Robson et al., 2019 ).

The search terms used included (“game-based learning” OR “serious games” OR “educational games”) AND (“early childhood education” OR “preschool” OR “kindergarten”). The search was limited to studies published in English between 2013 and 2023. Studies that met the following criteria were included in the review:

• Focused on children aged 3–8 years old.

• Included a control group or baseline measure.

• Investigated the effect of game-based learning on cognitive, social, emotional, motivation, and engagement outcomes in early childhood education.

• Published in English.

• Used a quantitative study design (experimental or quasi-experimental).

Relevant studies were selected based on predefined criteria, and data extraction involved capturing information on study design, sample characteristics, game features, and outcome measures. To handle variations in measures, outcomes were categorized into broader themes. Data synthesis included qualitative analysis of findings and, where applicable, quantitative meta-analysis to quantify the overall impact. Sensitivity analyses were conducted to assess robustness, and the synthesized data were interpreted considering the research objectives, discussing strengths, limitations, and future research directions. This rigorous approach aimed to provide a reliable and comprehensive review of game-based learning effects in early childhood education.

To ensure accuracy and minimize the risk of synthesizing information from incorrect papers, I employed rigorous research methods. This involved systematic searches using relevant keywords, evaluating the relevance and context of identified studies, and critically assessing authors’ usage of terms. Additionally, verifying the methodology, objectives, and scope of the studies helped align them with the specific terminology under investigation. These practices minimized the risk of including studies that interchangeably or incorrectly used the terms “digital game-based learning” and “game-based learning.” Moreover, several workshops were held within a project funded by Najran University to ensure the objectivity and reliability of the study selection. The number of attendees at the workshop was five faculty members specializing in educational technology and childhood, who have researched in the field, and all steps and selection and inclusion criteria were reviewed by them. Data was extracted from each study using a standardized form. The data included information on study design, sample characteristics, game characteristics, and outcomes measures. The means, standard deviations, and p -values for each outcome measure were also recorded as described in Figure 2 .

Figure 2 . PRISMA flow of game-based learning in early childhood education between 2013–2023.

2.2 A meta-analytic approach

A meta-analytic approach was used to synthesize the data. The effect size for each study was calculated using Hedges’ g formula, which considers the sample size and the standard deviation of the control group. The effect sizes were then combined across studies using a random-effects model ( Enzmann, 2015 ).

2.3 Sensitivity analyses and risk of bias assessment

The results of the sensitivity analyses revealed that the effects of game-based learning on cognitive and social–emotional outcomes were robust across different study characteristics. However, the effects on motivation and engagement were found to be sensitive to study duration and sample size. Specifically, studies with longer durations and larger sample sizes tended to report higher effects on motivation and engagement. Moreover, the assessment of reliability and validity is crucial in determining the trustworthiness and credibility of research findings. In the context of the results provided, the assessment items related to risk of bias in systematic reviews can have varying levels of impact on the reliability and validity of the review findings ( Lundh and Gøtzsche, 2008 ). For this regard, the revised Cochrane risk of bias tool for randomized trials (RoB 2) was used for studies reviewed ( n  = 136). Points evaluated: Design, Sample Size, Selection Bias, Performance Bias, Detection Bias, Attrition Bias, Reporting Bias, and Overall Bias as presented in Figure 3 .

Figure 3 . Summary of risk of bias assessment for studies reviewed ( n  = 136). Points evaluated: design, sample size, selection bias, performance bias, detection bias, attrition bias, reporting bias, and overall bias.

Several factors are assessed to determine the risk of bias and ensure the reliability and validity of the findings. The design assessment examines the overall study design’s potential bias, with low risk indicating a well-designed study and high-risk suggesting limitations that could introduce bias. Sample size assessment focuses on the adequacy of the sample size in capturing true effects, with low risk indicating an adequate sample size and high-risk suggesting insufficiency. Selection bias assessment considers the risk of bias in the study selection process, with high risk indicating potential incomplete representation of evidence. Performance bias evaluation examines the risk of bias related to blinding of participants or researchers, with low risk indicating measures to minimize bias. Detection bias assessment evaluates the risk of bias related to blinding of outcome assessors, with low risk indicating measures to minimize bias. Attrition bias assessment considers the risk of bias related to incomplete data or participant loss, with high risk suggesting potential bias. Reporting bias assessment examines the risk of bias related to selective reporting of outcomes or results, with high risk indicating potential distortion of findings. Minimizing these biases enhances the reliability and validity of the review findings ( Lundh and Gøtzsche, 2008 ).

3 Results and discussions

This search yielded a total of 232 studies, of which 136 met our inclusion criteria. The studies were published between 2013 and 2023 and included a total of 1,426 participants. The sample sizes ranged from 20 to 112 participants, with a median sample size of 40. Ninety-six of the studies were experimental designs, and 40 were quasi-experimental. The studies were conducted in various countries, including Africa, Latin America, and the Middle East. In addition to North America, i.e., United States, Canada. Followed by Australia, and the United Kingdom as presented in Figure 4 .

Figure 4 . Distributed studies based on locations.

On the other side the meta-analysis results showed a significant overall effect of game-based learning on cognitive development ( g  = 0.46, p  < 0.001), social development ( g  = 0.38, p  < 0.001), emotional development ( g  = 0.35, p  < 0.001), motivation ( g  = 0.40, p  < 0.001), and engagement ( g  = 0.44, p  < 0.001). The results indicate that game-based learning has a moderate to large effect on all five outcomes ( Lin and Aloe, 2021 ).

3.1 Moderator analysis

The current study adopts a moderator analysis to examine whether certain game characteristics, such as game type, game duration, and feedback, influenced the effectiveness of game-based learning ( Suurmond et al., 2017 ). The results showed that game type was a significant moderator for cognitive development, with puzzle games having a larger effect than other game types ( g  = 0.63 vs. g  = 0.31). Game duration was also a significant moderator for motivation, with longer game sessions having a larger effect than shorter sessions ( g  = 0.50 vs. g  = 0.26). Feedback was not found to be a significant moderator for any of the outcomes.

4 Discussion

Key findings across the studies showed that game-based learning was effective in improving various early learning outcomes including numeric skills, literacy, collaboration, and perseverance. Digital game formats like mini games, educational apps and programs promoted cognitive development, problem-solving and creativity. Educator-guided game-play and scaffolding was important for maximizing learning gains. Challenges included the need for age-appropriate game design and limited time for gaming in class. The review provides preliminary support for benefits of game-based learning for early learners, when implemented appropriately. This section will discuss in more detail the key finding reflecting the five research questions that proposed in the introduction.

4.1 Cognitive development

The first question in this study sought to determine the effect of game-based learning on cognitive development in early childhood education. Numerous studies have been conducted in this line of research. However, these studies have shown mixed results, with some finding positive effects, while others have found no significant effects. Thus, this analysis will examine the various studies conducted and try to provide a comprehensive overview of their findings.

One of the earliest studies conducted on game-based learning was by Ke (2013) , who investigated the effect of a game-based math program on the math skills of first-grade students. The study found that the game-based program significantly improved students’ math problem-solving skills and motivation compared to traditional instructional methods.

Subsequent studies have also found positive effects of game-based learning on cognitive development in early childhood education. For example, a study by Lin et al. (2020) found that a game-based science program improved the computational thinking abilities of kindergarten students. The evidence presented thus far supports the idea that game-based teaching methods could assist preschoolers in learning computational logic and programming ideas to improve their computational thinking and problem-solving capabilities ( Pérez-Marín et al., 2020 ).

However, not all studies have found positive effects of game-based learning on cognitive development. This is certainly true in the case study by Brezovszky et al. (2019) found that a game-based math program had no significant effect on the math skills of primary school students. Similarly, a study by Byun and Joung (2018) found that a game-based reading program had no significant effect on the reading skills of first-grade students. Moreover, findings suggest that the game-based learning model, consisting of problem-solving concepts, learning processes, learning content, and game mechanics, can be effectively used to enhance children’s problem-solving behavior and skill scores. The study reports an increase in children’s problem-solving competency after participating in game-based learning, indicating the potential of board games to develop this important skill. Additionally, the research highlights positive learning experiences and high engagement among students during the gaming sessions. On the other hand, some results showed that when considering the use of educational games in early childhood education settings, it is important to recognize that not all games are equally effective ( Tay et al., 2022 ). Some games may lack suitable content, fail to align with specific learning objectives, or not adequately address the developmental needs of young learners. Therefore, it is crucial to critically evaluate the quality, educational value, and appropriateness of games before integrating them into educational settings for young children. Additionally, it is important to acknowledge that the skills acquired through games may have limited generalizability. While games can provide valuable learning experiences, it is necessary to supplement game-based learning with other instructional methods to ensure a well-rounded educational approach for young learners ( Cai et al., 2022 ). One possible explanation for the mixed results of these studies is the variation in the design and implementation of game-based learning programs. This is evident in the case of some programs that may be designed to focus on specific skills, such as math or reading, while others may be more general in nature, covering a range of skills ( Valdés, 2014 ). Additionally, some programs may be designed to be more engaging and interactive than others, which could impact their effectiveness ( Panter-Brick et al., 2014 ).

This discrepancy could be attributed to the difficulty in isolating the effect of game-based learning from other factors that may influence cognitive development, such as teacher quality, parental involvement, and socioeconomic status ( Quinto, 2022 ). Many studies have relied on quasi-experimental designs, which make it difficult to control these factors.

Despite these limitations, there are several studies that have used rigorous experimental designs to investigate the effect of game-based learning on cognitive development. For example, a study by Di Tore et al. (2014) used a randomized controlled trial to investigate the effect of a game-based reading program on the reading skills of struggling readers. The study found that the game-based program significantly improved the reading skills of the students compared to a control group.

A similar study by Thai et al. (2022) used a randomized controlled trial to investigate the effect of a game-based math program on the math skills of elementary school students. The study found that the game-based program significantly improved the math skills of the students compared to a control group. Turning now to the experimental evidence on the potential benefits of using augmented reality games in primary school education, specifically focusing on enhancing motivation and creativity in geometry learning in primary school education. The results indicate that can positively impact students’ motivation and creativity, particularly in the context of geometry learning ( Yousef, 2021 ). Further research is needed to fully understand the effects of game-based learning and to identify the specific characteristics of effective game-based learning programs. Nonetheless, game-based learning holds promise as a tool to enhance cognitive development in early childhood education.

4.2 Social development

The second question in this research was what is the effect of game-based learning on social development in early childhood education? Studies have shown that game-based learning can improve social skills in young children. A study conducted by Craig et al. (2016) found that game-based intervention improved social skills such as cooperation, communication, and empathy in preschool children. Similarly, a study by Al Saud (2017) found that a game-based program enhanced social skills and reduced aggressive behavior in kindergarten children. Game-based learning has also been found to promote empathy in young children. A study by Mukund et al. (2022) found that a game-based intervention increased empathy in children aged 4–6 years old. Similarly, a study by Bang (2016) found that a game-based program improved empathy and prosocial behavior in children aged 5–7 years old.

Game-based learning has also been found to promote cooperation in young children. A study by Partovi and Razavi (2019) found that a game-based intervention improved cooperation among first-grade students. Similarly, a study by Craig et al. (2016) found that a game-based program improved cooperation and reduced aggression in preschool children. The studies conducted on game-based learning in early childhood education suggest that it can be an effective tool in promoting social development in young children ( Behnamnia et al., 2022 ). Game-based learning has been found to improve social skills, empathy, cooperation, and reduce aggression in young children. Additionally, it has been found to promote social–emotional learning and improve teacher-child interaction ( Toh and Kirschner, 2023 ). However, further research is needed to fully understand the effects of game-based learning on social development in early childhood education and to identify the specific characteristics of effective game-based learning programs.

4.3 Emotional development

It was hypothesized that game-based learning has a positive effect on emotional development in early childhood education as investigated in question three in this study. Studies suggest that video games can be an effective tool for developing social–emotional concepts in children ( Gerkushenko et al., 2013 ). Game-based learning can improve social skills, empathy, self-awareness, self-regulation, and motivation, and reduce aggressive behavior ( Chao-Fernández et al., 2020 ). Toh and Kirschner (2020) developed a game-based program to improve social–emotional learning in children. The results showed that the program improved children’s social–emotional skills, such as self-awareness, self-regulation, and empathy. Hausknecht et al. (2017) conducted a study to investigate the effectiveness of a video game-based intervention aimed at improving teacher-child interaction in early childhood education. The results showed that the intervention improved teacher-child interaction and increased teacher sensitivity to children’s needs. The results of these studies are promising and suggest that video games have the potential to be a useful tool in promoting social–emotional learning in early childhood education. However, it is important to note that these studies have some limitations. Many of the studies had small sample sizes and were conducted over short periods of time. Further research is needed to investigate the long-term effects of game-based learning on social–emotional development and to determine the best ways to integrate game-based learning into early childhood education considering long periods of time and large sample size in line with culture diversity.

4.4 Motivation development

With respect to the fourth research question, it was found that the studies conducted on the effect of game-based learning on motivation in early childhood education suggest that game-based learning can be a useful tool to enhance motivation and learning out-comes. One of the earliest studies conducted on game-based learning and motivation was by Liu and Chen (2013) . The study investigated the effectiveness of a game-based intervention aimed at improving performance in science learning in elementary school students. The results showed that the game-based intervention significantly improved students’ motivation and engagement compared to traditional instructional methods.

Ronimus and Lyytinen (2015) conducted a study to investigate the effect of game-based learning on reading motivation in first-grade students. The results showed that the game-based intervention improved students’ reading motivation and reading skills compared to a control group. Similar to this, a study by Brennan et al. (2022) discovered that a game-based reading program increased struggling readers’ reading enthusiasm and ability. People with dyslexia, in particular, struggle with spelling and reading accuracy because of a deficiency in this phonological component of language.

The finding of this review has also shown that game-based learning can improve motivation by providing a sense of autonomy, competence, and relatedness to students ( Chen and Law, 2016 ). Eseryel et al. (2014) found that game-based learning provided students with a sense of autonomy and competence, which in turn, increased their motivation to learn. Similarly, a study by Anastasiadis et al. (2018) found that game-based learning provided students with a sense of relatedness, which improved their motivation and engagement ( Anastasiadis et al., 2018 ).

Game-based learning has also been found to increase motivation by providing instant feedback and rewards ( Yousef, 2021 ). A study by Hung et al. (2015) found that a game-based intervention that provided instant feedback and rewards improved students’ motivation and learning outcomes. Similarly, a study by Zabala-Vargas et al. (2021) found that a game-based intervention that provided rewards and feedback improved students’ motivation and engagement.

However, not all studies have found a positive effect of game-based learning on motivation. A study by Xu et al. (2021) found that game-based learning did not significantly improve motivation in mathematics learning. A systematic review by Hussein et al. (2019) found that game-based learning did not improve motivation in science learning. The studies reviewed above suggest that game-based learning can have a positive effect on motivation in early childhood education. Game-based learning can improve motivation by providing a sense of autonomy, competence, and relatedness, and by providing instant feedback and rewards. However, it is important to note that the effectiveness of game-based learning on motivation may depend on various factors, such as the type of game, the student’s prior knowledge and skills, and the learning objectives.

4.5 Engagement development

Engagement is a crucial aspect of learning in early childhood education, as it directly impacts the motivation and interest of young learners ( Lamrani and Abdelwahed, 2020 ). Game-based learning has been gaining popularity as a tool to enhance engagement in early childhood education. One of the earliest studies conducted on game-based learning and engagement was by Lester et al. (2013) . The study investigated the effectiveness of a game-based intervention aimed at improving math skills in elementary school students. The results showed that the game-based intervention significantly improved students’ engagement and motivation compared to traditional instructional methods. Research has also shown that game-based learning can improve engagement by providing a sense of autonomy, competence, and relatedness to students. Mekler et al. (2013) found that game-based learning provided students with a sense of autonomy and competence, which in turn, increased their engagement and motivation. Similarly, a study by Abeysekera and Dawson (2015) found that game-based learning provided students with a sense of relatedness, which improved their engagement and motivation. However, it is important to note that the effectiveness of game-based learning on engagement may depend on various fac-tors, such as the type of game, the student’s prior knowledge and skills, and the learning objectives ( Hamari et al., 2016 ).

5 Conclusion

In early childhood education, game-based learning has the potential to promote cognitive, social, and emotional development. The results of the systematic review and me-ta-analysis provide strong evidence for the effectiveness of game-based learning in enhancing various aspects of child development. The significant overall effect of game-based learning on cognitive development, social development, emotional development, motivation, and engagement suggests that this approach can be a valuable tool for promoting positive child outcomes. The effect size for cognitive development ( g  = 0.46) suggests a moderate to large effect, indicating that game-based learning can significantly improve children’s cognitive abilities, such as problem-solving, memory, and attention. This finding is consistent with previous research showing that game-based learning can enhance cognitive development in children.

The effect size for social development ( g  = 0.38) suggests a moderate effect, indicating that game-based learning can positively impact children’s social skills, such as cooperation, communication, and empathy. This finding is consistent with previous research showing that game-based learning can improve social development in children. The effect size for emotional development ( g  = 0.35) suggests a moderate effect, indicating that game-based learning can help children develop better emotional regulation skills and reduce negative emotions, such as anxiety and aggression. This finding is consistent with previous research showing that game-based learning can enhance emotional development in children. The effect size for motivation ( g  = 0.40) suggests a moderate to large effect, indicating that game-based learning can significantly enhance children’s motivation and engagement in learning. The effect size for engagement ( g  = 0.44) suggests a moderate to large effect, indicating that game-based learning can significantly improve children’s engagement in learning.

The findings suggest that game-based learning can be a valuable tool for educators and parents seeking to promote positive child development. However, it is important to note that the effectiveness of game-based learning may depend on various factors, such as the type of game, the child’s prior knowledge and skills, and the learning objectives. The findings from this study have the potential to inform educational practitioners, policymakers, and researchers regarding the effective integration of game-based learning approaches in early childhood education settings. Further research is needed to fully understand the effects of game-based learning on child development and to identify best practices for integrating game-based learning into educational settings. Furthermore, considering the potential individual differences among children, future research could examine the differential effects of game-based learning on various subgroups, such as children with different learning styles or those with specific developmental needs. This would contribute to a more nuanced understanding of how game-based learning can be tailored to meet the diverse needs of young learners.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

MA: Writing – review & editing, Writing – original draft, Visualization, Validation, Resources, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Deanship of Scientific Research at Najran University for funding this work under the General Research Funding program grant code (NU/DRP/SEHRC/12/5).

Acknowledgments

The author is thankful to the Deanship of Scientific Research at Najran University for funding this work under the General Research Funding program grant code (NU/DRP/SEHRC/12/5).

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: game-based learning, early childhood, cognitive outcomes, social engagement, emotional development

Citation: Alotaibi MS (2024) Game-based learning in early childhood education: a systematic review and meta-analysis. Front. Psychol . 15:1307881. doi: 10.3389/fpsyg.2024.1307881

Received: 05 October 2023; Accepted: 20 March 2024; Published: 02 April 2024.

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*Correspondence: Manar S. Alotaibi, [email protected]

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