Training of Executive Functions in Children: A Meta-Analysis of Cognitive Training Interventions
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| Title: | Training of Executive Functions in Children: A Meta-Analysis of Cognitive Training Interventions |
|---|---|
| Language: | English |
| Authors: | Efsun Birtwistle (ORCID |
| Source: | SAGE Open. 2025 15(1). |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 18 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Information Analyses |
| Education Level: | Elementary Education Junior High Schools Middle Schools Secondary Education |
| Descriptors: | Preschool Children, Elementary School Students, Middle School Students, Executive Function, Short Term Memory, Cognitive Development, Numeracy, Literacy, Age Differences, Age Groups, Child Development, Student Development, Computer Assisted Instruction, Handheld Devices, Feedback (Response), Early Intervention |
| DOI: | 10.1177/21582440241311060 |
| ISSN: | 2158-2440 |
| Abstract: | We investigated the effect of cognitive training of executive functions on children's cognitive outcomes. To address this issue, a systematic meta-analysis of published research articles on cognitive training interventions was performed considering children's age, training duration, -procedure, and -technology in moderator analyses. The results (N = 57) of a random-effects-model showed that cognitive training was effective with a total effect size of g = 23. The training was more effective for younger compared to older children. Training benefits were found for near- and far-transfer tasks. The largest gains for a near-transfer skill were found for working memory. Both numeracy and literacy skills profited from training. Computer training was very effective, however, only a few studies used mobile technology. Non-adaptive training was associated with greater effect sizes and both group and individual training were similarly effective. Verbal feedback was important for younger children. School was an effective context for training, however, only a few studies were conducted at home or at the lab. The findings are discussed and advocate an early start of cognitive training interventions. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1466942 |
| Database: | ERIC |
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| FullText | Links: – Type: pdflink Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwGvhw0Y_p1MqrLRf1-jay12AAAA4jCB3wYJKoZIhvcNAQcGoIHRMIHOAgEAMIHIBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDEAqps9uMAomRsJQFQIBEICBmgLg8Zmcuom-iApacThYDM2pv3Z0IIfx9t6QubpxiVWEjXI1Fx4aR1-djU5TXKBA12Mf_7FG5PFrlRtp2yZiqE8jPISTUQrAeehwQCHPjCIhgs6kV2i-XOlhrLvflDoB2yAb5M_JP_UwNYfx1NpNg2JaPZlQuZtc37WEV6fVRxS_AFG2rgQ8OGrYklIRW-0Ru_52K8efuEYUrpE= Text: Availability: 1 Value: <anid>AN0184162251;[kbz6]01jan.25;2025Apr03.02:11;v2.2.500</anid> <title id="AN0184162251-1">Training of Executive Functions in Children: A meta-analysis of cognitive training interventions </title> <p>We investigated the effect of cognitive training of executive functions on children's cognitive outcomes. To address this issue, a systematic meta-analysis of published research articles on cognitive training interventions was performed considering children's age, training duration, -procedure, and -technology in moderator analyses. The results (N = 57) of a random-effects-model showed that cognitive training was effective with a total effect size of g = 23 The training was more effective for younger compared to older children. Training benefits were found for near- and far-transfer tasks. The largest gains for a near-transfer skill were found for working memory. Both numeracy and literacy skills profited from training. Computer training was very effective, however, only a few studies used mobile technology. Non-adaptive training was associated with greater effect sizes and both group and individual training were similarly effective. Verbal feedback was important for younger children. School was an effective context for training, however, only a few studies were conducted at home or at the lab. The findings are discussed and advocate an early start of cognitive training interventions.</p> <p>Keywords: systematic review; meta-analysis; cognitive training of children; executive functions; age differences; training interventions</p> <hd id="AN0184162251-2">Introduction</hd> <p>In everyday life, our cognitive system is processing and managing various environmental information and here executive functions (EFs) play a crucial role. While EFs improve in time, the question remains if and how they may best be trained for developing children. In this paper, we will try to answer this question and consider numerous relevant moderator variables that may influence the effectiveness of cognitive training interventions.</p> <hd id="AN0184162251-3">The Development of EFs</hd> <p>The development of EFs starts early in life and undergoes profound changes in line with the structural and functional development of associated brain regions ([<reflink idref="bib123" id="ref1">123</reflink>]). They are supported by higher order brain areas which are described as the slowest to mature throughout childhood and puberty ([<reflink idref="bib18" id="ref2">18</reflink>]; [<reflink idref="bib42" id="ref3">42</reflink>]). EFs are strongly interconnected with the Prefrontal cortex (PFC; [<reflink idref="bib36" id="ref4">36</reflink>]) a brain area involved in behaviour planning, decision making, and social behaviour ([<reflink idref="bib28" id="ref5">28</reflink>]; [<reflink idref="bib32" id="ref6">32</reflink>]). The formation of the PFC peaks between twelve months and four years of age ([<reflink idref="bib18" id="ref7">18</reflink>]; [<reflink idref="bib109" id="ref8">109</reflink>]) and continues to develop through adolescence until early adulthood ([<reflink idref="bib24" id="ref9">24</reflink>]). This process marks the importance of the early years for children to form and improve their EF skills ([<reflink idref="bib27" id="ref10">27</reflink>]; [<reflink idref="bib40" id="ref11">40</reflink>]).</p> <p>Whereas attentional control skills start to emerge in infancy throughout early childhood, planning and cognitive flexibility mature in later years ([<reflink idref="bib3" id="ref12">3</reflink>]). Impairment in EFs may result in various behavioural impairments during different stages of life ([<reflink idref="bib26" id="ref13">26</reflink>]) such as task-based impairment, emotional dysfunction, or response-disinhibition ([<reflink idref="bib3" id="ref14">3</reflink>]; [<reflink idref="bib20" id="ref15">20</reflink>]; [<reflink idref="bib104" id="ref16">104</reflink>]). These difficulties do not only occur in adolescents or adults, but also in early years of life ([<reflink idref="bib4" id="ref17">4</reflink>]) which emphasizes that impaired EFs may impact children's development of academic, social, and personal skills.</p> <p>Starting from preschool years, there is an exponential development of EFs, especially between the ages three and six, and mastering EFs takes time until adulthood ([<reflink idref="bib18" id="ref18">18</reflink>]; [<reflink idref="bib24" id="ref19">24</reflink>]). During the developmental process, different age groups acquire and process information differently ([<reflink idref="bib49" id="ref20">49</reflink>]). For example, in a latent variable analysis with four age groups (<reflink idref="bib7" id="ref21">7-</reflink>, 11-, 15-, and 21- years old), [<reflink idref="bib50" id="ref22">50</reflink>] found different developmental trends in children's EF outcomes. However, early years (e.g., before 7 years) were not considered. Based on developmental and normative studies, one review article ([<reflink idref="bib3" id="ref23">3</reflink>]) revealed that age is an important factor to consider when testing EFs, while another review article suggested to focus across a larger age span to understand the developmental trajectories better ([<reflink idref="bib9" id="ref24">9</reflink>]). These studies stress the importance of including age as a factor in EF research as there is still a developmental research gap.</p> <hd id="AN0184162251-4">Precursor EF Components of Cognitive and Academic Skills</hd> <p>Three separable components can be characterized for EFs, even though they are moderately correlated and may not always be valid for young children ([<reflink idref="bib6" id="ref25">6</reflink>]; [<reflink idref="bib26" id="ref26">26</reflink>]): Working memory (WM), inhibitory control (IC), and cognitive flexibility (CF). These are essential components for higher-order functions and linked to children's attainment ([<reflink idref="bib76" id="ref27">76</reflink>]). This includes literacy skills such as writing, spelling, and syntactic acquisition of language ([<reflink idref="bib2" id="ref28">2</reflink>]; [<reflink idref="bib30" id="ref29">30</reflink>]; [<reflink idref="bib80" id="ref30">80</reflink>]) as well as numerical skills such as calculation, problem solving and conceptual knowledge ([<reflink idref="bib21" id="ref31">21</reflink>]; [<reflink idref="bib23" id="ref32">23</reflink>]; [<reflink idref="bib105" id="ref33">105</reflink>]). For instance, [<reflink idref="bib2" id="ref34">2</reflink>] analysed the association between EFs and literacy skills in a longitudinal study with school-aged children and found that developing EFs, particularly inhibition and shifting, are associated with literacy outcomes. [<reflink idref="bib1" id="ref35">1</reflink>] investigated the predictive role of both WM and intelligence on children's academic skills and found that WM at school entry is a more powerful predictor of later academic success than fluid intelligence.</p> <p>For preschool children, EFs are associated with literacy outcomes such as orthographic knowledge stored in memory ([<reflink idref="bib94" id="ref36">94</reflink>]). In their longitudinal study, [<reflink idref="bib119" id="ref37">119</reflink>] showed that WM predicted both domain-specific and domain-general skills making unique contributions to children's academic success in preschool. [<reflink idref="bib76" id="ref38">76</reflink>] found that WM was a reliable predictor of academic achievement both in early and late developmental stages.</p> <p>Similarly, EFs have an impact on mathematical attainment from very early ages until adulthood (e.g., [<reflink idref="bib23" id="ref39">23</reflink>]; [<reflink idref="bib73" id="ref40">73</reflink>]). [<reflink idref="bib79" id="ref41">79</reflink>] showed that both domain-specific and domain-general factors predicted math achievement during pre-school and early primary school. Further, WM and processing speed predicted numeracy competence, especially during pre-school years. [<reflink idref="bib73" id="ref42">73</reflink>] examined the predictive role of EFs on both literacy and math skills in a longitudinal study design. Here, early EFs predicted emergent literacy and numeracy skills in two- and five-years old children. Inhibitory control ([<reflink idref="bib31" id="ref43">31</reflink>]) and shifting were also linked to academic performance ([<reflink idref="bib122" id="ref44">122</reflink>]).</p> <p>Several studies established a closer link between poor executive functions and learning difficulties, memory deficits and attention-related difficulties ([<reflink idref="bib39" id="ref45">39</reflink>]; [<reflink idref="bib67" id="ref46">67</reflink>]; [<reflink idref="bib68" id="ref47">68</reflink>]). For this reason, researchers have been interested in interventions for children to improve their cognitive skills and capacity, especially for children who have learning difficulties or show impulsive behaviour ([<reflink idref="bib60" id="ref48">60</reflink>]). In recent years, cognitive training studies and the usage of advanced technical methods in children's training have become increasingly popular among researchers ([<reflink idref="bib103" id="ref49">103</reflink>]). Here, not only children with learning difficulties but also typically developing children are targeted. New technological possibilities have led to novel, stimulating and entertaining educational tools for children to boost their learning and cognitive competencies (e.g., [<reflink idref="bib74" id="ref50">74</reflink>]; [<reflink idref="bib75" id="ref51">75</reflink>]).</p> <hd id="AN0184162251-5">Cognitive Training of Executive Functions</hd> <p>Executive functions can be trained throughout the life span ([<reflink idref="bib110" id="ref52">110</reflink>]; [<reflink idref="bib61" id="ref53">61</reflink>]; [<reflink idref="bib59" id="ref54">59</reflink>]). Cognitive training interventions have important implications in both clinical and educational settings, especially for children who show cognitive-, social-, or academic-impairments. While not all training procedures seem to work well to support children's learning, findings from several interventions show the potential to improve children's learning ([<reflink idref="bib55" id="ref55">55</reflink>]; [<reflink idref="bib112" id="ref56">112</reflink>]).</p> <hd id="AN0184162251-6">Potential Moderators of Cognitive Training</hd> <p>Potential transfer effects of training gains across age groups are heavily debated ([<reflink idref="bib70" id="ref57">70</reflink>]; [<reflink idref="bib90" id="ref58">90</reflink>]; [<reflink idref="bib91" id="ref59">91</reflink>]). We are missing comprehensive studies on general EFs and their association with academic success for different age groups that consider various potentially moderating factors and their educational impact, although some first attempts with a different research focus have been conducted ([<reflink idref="bib70" id="ref60">70</reflink>]; [<reflink idref="bib93" id="ref61">93</reflink>]; [<reflink idref="bib95" id="ref62">95</reflink>]).</p> <p>Training duration is seldom considered even though it could be a key factor to define the best possible training duration for children across different age groups ([<reflink idref="bib93" id="ref63">93</reflink>]). Computer-based methods and experimental approaches are often used in interventions, however, we do not yet know, whether these approaches are best suited for children's learning and education. Due to the worldwide Covid-19 pandemic, other methods have become important as children are now exposed to mobile technologies more frequently than ever ([<reflink idref="bib43" id="ref64">43</reflink>]).</p> <p>Training procedures such as sample training strategies, training context and feedback were not focused on prior reviews and meta-analyses (but also see [<reflink idref="bib93" id="ref65">93</reflink>]). All these potential moderators should be investigated separately for different age groups to get a better insight about successful trainings and ways to improve cognitive training interventions.</p> <p>In their meta-analyses, [<reflink idref="bib70" id="ref66">70</reflink>] and [<reflink idref="bib93" id="ref67">93</reflink>] considered factors such as age, transfer effects, and training dose. Their reviews and analyses were mainly focused on improvements based on WM training as part of EFs. However, it is widely acknowledged that adaptations to changing task requirements and action modulation also require further and complex executive control processes such as planning, inhibition, and task-switching, especially for developing children ([<reflink idref="bib56" id="ref68">56</reflink>]). On the contrary, [<reflink idref="bib95" id="ref69">95</reflink>] used general EF training in their analysis. However, their age range was limited (i.e., 3–6 years). Further, they included all studies with more than ten sessions instead of considering different training durations. Consequently, they did not consider age differences and different training durations and their potential associations with other educational factors such as feedback and context effect. The current study uses a comprehensive approach to analyse potential moderators of cognitive training interventions for children.</p> <hd id="AN0184162251-7">Transfer Effects</hd> <p>Cognitive training of EFs before and after the start of formal education impacts on various cognitive and academic outcomes and gives children the possibility to advance their skills in similar or related competencies. These transfer effects are divided into two significant categories as <emph>near-</emph> and <emph>far-transfer</emph>. It has previously been suggested that transfer of learned information is possible if there are similar task-features or common elements which makes the transfer more likely for near-transfer, and less likely for far-transfer skills ([<reflink idref="bib111" id="ref70">111</reflink>]). However, coupling of several cognitive processes such as attention, WM, and problem solving through training underlines the likelihood of potential cross-domain transfer effects (also see primitive elements theory; [<reflink idref="bib113" id="ref71">113</reflink>]). Therefore, investigating far-transfer skills has become a focus of research ([<reflink idref="bib103" id="ref72">103</reflink>]).</p> <p>Whereas some cognitive training tasks are claimed to be effective in supporting children's near-and far-transfer skills ([<reflink idref="bib87" id="ref73">87</reflink>]; [<reflink idref="bib110" id="ref74">110</reflink>]; [<reflink idref="bib124" id="ref75">124</reflink>]), other studies addressed the opposite, suggesting that cognitive training may not be as effective, and that maintaining training benefits may be difficult ([<reflink idref="bib37" id="ref76">37</reflink>]; [<reflink idref="bib70" id="ref77">70</reflink>]; also see [<reflink idref="bib13" id="ref78">13</reflink>]).</p> <p>On the one hand, some research reported benefits after cognitive training interventions for both near and far-transfer skills (e.g., [<reflink idref="bib95" id="ref79">95</reflink>]; [<reflink idref="bib108" id="ref80">108</reflink>]; [<reflink idref="bib120" id="ref81">120</reflink>]). On the other hand, cognitive training was found to generate either small or no transfer effect, specifically for far-transfer skills, which may be due to underpowered samples, training type and duration, and age factor ([<reflink idref="bib25" id="ref82">25</reflink>]; [<reflink idref="bib91" id="ref83">91</reflink>]; [<reflink idref="bib98" id="ref84">98</reflink>]; [<reflink idref="bib116" id="ref85">116</reflink>]). Another reason for such findings may be that far-transfer skills often take more time to emerge and thus even longer training periods are needed ([<reflink idref="bib83" id="ref86">83</reflink>]). To tackle this problem, it is important to understand the transfer effects for cognitive training success/failure.</p> <hd id="AN0184162251-8">Age</hd> <p>Age differences were found for various task outcomes ([<reflink idref="bib50" id="ref87">50</reflink>]; [<reflink idref="bib81" id="ref88">81</reflink>]). While the effectiveness of EF training for younger versus older pre-schoolers did not differ across studies ([<reflink idref="bib95" id="ref89">95</reflink>]), younger compared to older children benefited more from training of verbal WM, and no age differences were found for far-transfer skills ([<reflink idref="bib70" id="ref90">70</reflink>]). Younger compared to older children may indeed potentially benefit more from early training if no specific strategies are applied for learning ([<reflink idref="bib102" id="ref91">102</reflink>]; but also see [<reflink idref="bib72" id="ref92">72</reflink>]). Even though older children could potentially benefit more from learning strategies (e.g., through rehearsing task instructions), it is also important to determine how training benefits differ across children of different ages ([<reflink idref="bib17" id="ref93">17</reflink>]). Though, some studies report no differences after the age of six ([<reflink idref="bib52" id="ref94">52</reflink>]; [<reflink idref="bib115" id="ref95">115</reflink>]).</p> <hd id="AN0184162251-9">Training Duration</hd> <p>Conflicting findings on near- and far-transfer effects raise the question concerning training duration as potential training gains may manifest only after a certain amount of training time and there may be differences in neurocognitive processes in terms of learning duration ([<reflink idref="bib83" id="ref96">83</reflink>]).</p> <p>Research indicates that successful training programs should (at least) last between 4 and 8 weeks (with 2–5 times a week and &gt;10–60 mins per session) to be sufficient for children to experience short- and/or long-term training benefits ([<reflink idref="bib12" id="ref97">12</reflink>]; [<reflink idref="bib87" id="ref98">87</reflink>]). Other studies ([<reflink idref="bib37" id="ref99">37</reflink>]; [<reflink idref="bib96" id="ref100">96</reflink>]) found inconsistent results with a similar amount of training, but no long-term training benefits. These inconsistent findings make training duration another potential moderator in cognitive training.</p> <hd id="AN0184162251-10">Training Tool</hd> <p>Computer-based trainings are popular in cognitive training studies with children ([<reflink idref="bib7" id="ref101">7</reflink>]; [<reflink idref="bib88" id="ref102">88</reflink>]; [<reflink idref="bib110" id="ref103">110</reflink>]; [<reflink idref="bib118" id="ref104">118</reflink>]). Analogue (e.g., paper-and-pencil) training activities are another popular method used in training, especially in classroom environments ([<reflink idref="bib29" id="ref105">29</reflink>]; [<reflink idref="bib62" id="ref106">62</reflink>]). However, in recent years, there is a growing trend to support children's education through mobile learning applications ([<reflink idref="bib47" id="ref107">47</reflink>]; [<reflink idref="bib74" id="ref108">74</reflink>]). There is evidence that children can benefit from well-designed educational apps ([<reflink idref="bib8" id="ref109">8</reflink>]) even though not every game promotes young children's learning ([<reflink idref="bib53" id="ref110">53</reflink>]).</p> <p>There are advantages and disadvantages to all the methodologies. For example, while computer-based training may be applied in a controlled laboratory setting which, at least initially, increases children's attentional focus and provides researchers with a standardized environment, it may also reduce their motivation and engagement in long-term training designs (e.g., sitting in front of a computer for a long time or games that are not meaningful to children; see [<reflink idref="bib71" id="ref111">71</reflink>]). Mobile training apps offer the potential for children to be trained at home and at kindergarten/school. Further, they provide children with the flexibility to take the mobile device (e.g., tablet) in their hands and navigate in the game themselves, which may increase their motivation and engagement with only a little bit of scaffolding. However, this approach may not offer such a controlled environment as in computer-based interventions and it may be biased by confounding factors (e.g., attentional distraction). In the analogue methods, children perform similar paper-and-pencil activities as in their usual school/kindergarten activities. Here, children can easily be trained in a group led by a teacher/trainer, which can reduce costs and could be less time-consuming (<emph>note</emph>: group training may also be feasible through computer-based interventions given sufficient funding and logistics are provided for). However, these methods do not obtain behavioural measurements as accurately as in the computer- or mobile-based approaches (e.g., mobile sensing ([<reflink idref="bib10" id="ref112">10</reflink>]). Therefore, training tool is another moderator that may help to understand which training tool can be considered useful.</p> <hd id="AN0184162251-11">Training Procedure</hd> <p>Test adaptivity, sample training procedure (i.e., individual vs. group), training context (i.e., home vs. school vs. laboratory) and the provision of feedback (i.e., whether any feedback-verbal or visual-were provided during training) are potential moderators that should be considered. For instance, adaptive training has been shown to be effective in computer-based trainings ([<reflink idref="bib35" id="ref113">35</reflink>]) and both individual and group training revealed promising outcomes ([<reflink idref="bib86" id="ref114">86</reflink>]). Feedback on the trained tasks is considered important as it may support focused attention and enhance training benefits ([<reflink idref="bib93" id="ref115">93</reflink>]). Training context such as controlled (i.e., laboratory), semi-controlled (i.e., school) and naturalistic (i.e., home) environments were discussed to contribute differently to children's EF skills ([<reflink idref="bib69" id="ref116">69</reflink>]; [<reflink idref="bib97" id="ref117">97</reflink>]). Particularly, home-based training was found promising to improve children's competency development ([<reflink idref="bib76" id="ref118">76</reflink>]).</p> <hd id="AN0184162251-12">The Scope of the Current Study</hd> <p>In the present study, we focused on early EFs. Based on inconsistent findings and by considering the suggestions from previous studies and reviews, this systematic meta-analysis investigated whether cognitive training interventions using EF tasks improved general- and domain-specific skills of children while considering potential moderators such as transfer effects, age, training duration, training tool, and training procedure. We were interested in answering the following research questions:</p> <p></p> <ulist> <item> Q1: To what extent cognitive training of EFs is effective for children?</item> <p></p> <item> Q2: How does the effectiveness of cognitive training differ for younger vs. older children?</item> <p></p> <item> Q3: What are the differences across age groups in terms of near-and far-transfer skills?</item> <p></p> <item> Q4: What is the role of duration on children's outcomes across different age groups?</item> <p></p> <item> Q5: To what extent do the training tool, test adaptivity, context, feedback, and sample training procedure influence children's outcomes in different age groups?</item> </ulist> <p>We expected (H1) cognitive training to improve children's outcomes in general, and here (H2) young children to gain more from cognitive training as early interventions are often effective ([<reflink idref="bib70" id="ref119">70</reflink>]). Next, we hypothesized (H3) training gains in near-transfer skills, whereas for far-transfer skills we expected little or zero gains which would be in line with previous findings (e.g., [<reflink idref="bib37" id="ref120">37</reflink>]). We further assumed (H4) that longer trainings should be more effective, especially for younger children ([<reflink idref="bib51" id="ref121">51</reflink>]). Lastly, we expected (H5) to find greater training success of adaptive, computer-based, and individual training, that provides feedback and is home-based ([<reflink idref="bib93" id="ref122">93</reflink>]).</p> <hd id="AN0184162251-13">Methods</hd> <p></p> <hd id="AN0184162251-14">Search Strategy and Eligibility Criteria</hd> <p>Studies eligible for this meta-analysis included published empirical articles between &gt;1980 and 2024 (October) that used randomized controlled trials or quasi experiments with a treatment in which either a treated or untreated control group was also tested with pre-and post-test assessments. As we only used published articles in peer-reviewed journals, but not unpublished reports or manuscripts, we additionally ran analyses to estimate and correct for potential publication bias. We focused on reviewing and analysing cognitive training interventions that applied cognitive training of EFs with a single- (i.e., one EF component is trained) or mixed-tasks (i.e., different EF components are trained) which included interventions that applied general EF, WM, IC, or CF training.</p> <p>We defined our independent (IV) and dependent variables (DV). For the IV, we were interested to find studies that provided cognitive training targeting general EFs or components of EFs (i.e., single WM, IC, CF, or mixed-task interventions). We aimed to have a comprehensive approach toward EFs instead of focusing on a single EF component (e.g., WM only).</p> <p>We differentiated the DVs as near-and far- transfer outcomes (i.e., near-transfer: EF, WM, IC, CF, and attention tasks; far-transfer: literacy, numeracy, and reasoning tasks). Further, we included training studies targeting young and primary school children to investigate training benefits among children of different ages. Training tool was another criterion, and for this we were looking for studies using different training methods.</p> <p>We were particularly interested in the development and improvement of EFs and thus focussed on interventions starting from young ages until late-primary school years/early adolescence. To be able to do such analysis with age, we included articles that involved participants between three and fourteen years.</p> <p>Studies focusing on children with a confirmed diagnosis were excluded (e.g., ADHD, autism, etc.) as they were not the focus of the present study. Consequently, only studies that focused on typically developing children who were not clinically diagnosed were included (please note that socially disadvantaged children, low-achieving children, or children with mild learning problems were still included). For this reason, most of the studies included investigated typically developing (TD) children with the exceptions of mixed samples in some studies where most the children were TD children (<emph>N</emph> = 4).</p> <p>Training programs that lasted four or more than four weeks (at least twice or three times weekly) with at least 10 and 15 mins of training (&gt;80 mins) time were found to be sufficient for children to generate short- and/or long-term training benefits ([<reflink idref="bib7" id="ref123">7</reflink>]; [<reflink idref="bib12" id="ref124">12</reflink>]; [<reflink idref="bib11" id="ref125">11</reflink>]). Consequently, we only included articles that provided training for at least eighty minutes. Sometimes information about the sessions, weeks, and minutes were given approximately, so the calculation was performed with the information provided in the articles. Here, always the possible maximum training duration was considered.</p> <p>To be able to determine the training effect on child outcomes, we have taken aggregated data from primary studies and included studies that provided pre-test (time 1) and post-test (time 2) accuracy (items solved correctly) mean and index scores with standard deviations/standard errors (later all values were converted into SDs) so that an effect size could be computed. Outcome scores from children's assessments, parent, parent-teacher, and teacher ratings were used, but qualitative data (e.g., classroom observations) were not considered. Studies with missing data on child outcomes were excluded unless the contacted author/s had responded to our request and provided us with the relevant data. Reaction time data were excluded as many articles on cognitive training did not include reaction times in their analyses.</p> <p>We included studies with immediate post-training scores (i.e., the assessment that was conducted after the training had been finished (time 2), no follow-up -long-term- scores were included) to be able to understand the training benefits between time 1 and time 2 assessments. However, in some studies either the training took longer than other studies or post-test was carried out after a long break. Finally, we included children of samples independent of language, family, or cultural background.</p> <p>Accordingly, we performed a literature search in the following electronic databases: ERIC, PsycINFO, PubMEd, APA PsychInfo, PSYINDEX, Scopus and Google Scholar by using several search terms in different combinations (see Supplemental Appendix A<emph>for search terms and combinations</emph>). At the end, there was a pool of 1,528 articles which were uploaded in Endnote reference management software ([<reflink idref="bib15" id="ref126">15</reflink>]). All the duplicates were removed (<emph>N</emph> = 504). To optimize our title-, abstract- and full-text screening, we then started using Covidence (<emph>Covidence Systematic Review Software</emph>, [<reflink idref="bib22" id="ref127">22</reflink>]) as our main systemic review and screening manager. Through this software, each reviewer (<emph>N</emph> = 7) worked in collaboration for screening but independently to either accept or reject an article during title-, abstract- and full-text screening which helped to reduce the possibility of biased article selections. At the end, the project manager decided on the conflicting results. In the final step, we determined our eligible articles that were included in the meta-analysis (<emph>N</emph> = 57, see Supplemental Appendix B) and then finalized the screening process (see Figure 1).</p> <p>DIAGRAM: Figure 1. PRISMA Flow diagram of the process including identification, screening, exclusion and inclusion of studies for meta-analysis. Prisma diagram adapted from [<reflink idref="bib78" id="ref128">78</reflink>].</p> <hd id="AN0184162251-15">Coding and Data Entry</hd> <p>To conduct moderator analyses, we first prepared a literature coding file to be able to determine each study and identify the moderator variables. Two reviewers actively worked together, both independently and in collaboration, to retrieve data from the articles. The moderators were defined as: transfer effects, age, training duration, training tool, and training procedure. Transfer effects were categorised according to the outcomes of near- or far-transfer assessments. For example, general EF, WM, IC, CF, and attention tests were coded as "near-transfer," and literacy, numeracy and reasoning tests were coded as "far-transfer." As the WM component was the focus of many training studies within our eligibility criteria, and to further evaluate the training effects on specific WM outcomes, we additionally differentiated WM as general WM test (mixed WM tests), WM span backwards, WM span forwards, WM verbal, and WM visuospatial as reported in the articles. Study outcomes that used error rates or switching costs (e.g., CF tasks) were inverted for our analyses.</p> <p>Age was divided into three categories as younger- (Ages 4–6), early primary school- (Ages 7–9), and older school-aged (Ages 10–14) children to be able to analyse the variances across different age groups. For training intensity, we coded the duration of the sessions (i.e., how many sessions were provided), duration of weeks (i.e., for how many weeks the training was carried out), and duration of the whole training in minutes (i.e., this score was calculated by multiplying the duration of one single training session with the number of training sessions). With this approach, we were able to obtain the total training time in minutes, even though we were not able to calculate this total score for all the studies. Further, we split the studies into three groups with low (up 16.8 percentile, 1 SD below the mean), average (16.8–83.2 percentiles, M -/+ 1SD) and high training times (83.2 percentile and higher, 1 SD above the mean).</p> <p>Training tool was coded into three categories as computer-, mobile- and analogue-based methods. Test adaptivity were differentiated as to whether the training was given via adaptive or non-adaptive training regimes. That is, we were interested in whether the training task was adjusted by taking children's responses into account and by modifying the task accordingly (e.g., staircase; ([<reflink idref="bib114" id="ref129">114</reflink>]).</p> <p>Training feedback was categorised as whether there was any feedback. If there was feedback, whether the feedback was given visually or verbally by the researchers. Sample training procedure was based on group and individual training. Finally, training context was specified into three categories (home, school, and laboratory).</p> <hd id="AN0184162251-16">Results</hd> <p></p> <hd id="AN0184162251-17">Main Effects</hd> <p>The analyses were performed in R ([<reflink idref="bib107" id="ref130">107</reflink>]) with packages called "metafor" and "robumeta" right after collapsing the standardized mean scores together into one excel file to calculate the effect sizes ([<reflink idref="bib34" id="ref131">34</reflink>]). Here, the raw data from the studies were extracted and entered in an excel document. Next, this data was used to calculate individual effect sizes (i.e., hedges g, variance, and standard deviations (SDs) for each comparison across all the studies as random-effect model). Finally, we used R to aggregate the effects, visualize the distribution, and control for correlated samples.</p> <p>The differences between pre-and post-test scores (i.e., mean scores, SDs) were computed for experimental and control groups. To test the first two research questions, we first checked the efficacy of overall cognitive training interventions. A random-effects-model (<emph>N</emph> = 57) with robust variance estimation (RVE) was used for effect size estimation (Hedges <emph>g</emph>) as multiple results were retrieved from the same samples, and thus corrected for correlated samples. The RVE-correction was also used to address the problem of correlated samples and multiple effect sizes per study. Here, the effects coming from the same study were nested together to correct for correlated samples. We did not expect further dependencies and used the default option for RVE-correction in the "robumeta" package for summary and moderator analyses ([<reflink idref="bib34" id="ref132">34</reflink>]; Pustejovsky &amp; Tipton, 2022). With this approach, the analysis of fifty-seven studies revealed a small but positive significant overall effect of cognitive training interventions with Hedges <emph>g</emph> =.23 (<emph>SE</emph> =.07; <emph>p</emph> &lt;.01, CI [.09;.37]).</p> <p>We estimated the variance of the distribution of true effect sizes. The analysis identified high heterogeneity between intervention effects: <emph>T</emph><sups>2</sups> = 0.46 (<emph>SE</emph> =.04), <emph>I</emph><sups>2</sups> = 84.05%, <emph>H</emph><sups>2</sups> = <emph>6.27, Q</emph> (<emph>df</emph> = 511) = 1806.33, <emph>p</emph> &lt;.0001. As our aim was to include as many studies as possible that used various EF tasks and EF components, high heterogeneity across studies and applied tasks was expected. This finding emphasizes the importance of moderator analyses, which were conducted in the next step. Here, we expected to be able to explain significant part of this heterogeneity with the moderators introduced.</p> <p>Forest (Figure 2) and funnel (Figure 3) plots were used for visual examination of effect size distributions, to detect outlier studies and indicators of publication bias, and potentially questionable research practices. One study was identified as strong outlier and was excluded from further analysis. Visual examination, asymmetry test, or P-curve analysis (Figure 4) did not reveal any indication of publication bias. Therefore, the results of our random-effect model estimation were interpreted without further correction.</p> <p>Graph: Figure 2. Forest plot of the overall effect of cognitive training interventions (for study codes please see Supplemental Appendix B).</p> <p>Graph: Figure 3. Funnel plot of the overall effect of cognitive training interventions.</p> <p>Graph: Figure 4. P-curve to illustrate publication bias.</p> <hd id="AN0184162251-18">Moderator Analyses</hd> <p>A meta-regression and subsequent series of moderator analyses were performed to find answers to our research questions. The meta-regression showed that control variables (year of publication, type of control, etc.) were not significant predictors, which allowed us to have a look at general effect sizes and perform post-hoc analyses to identify actual effect sizes for each moderator of interest. The effect of cognitive training of EFs on children's outcomes were first estimated independent of children's age (see Supplemental Appendix C for overall results). Training benefits were found for both far- and near-transfer tasks when all age groups were combined with similar effect sizes; near-transfer, <emph>g</emph> = 0.22 (<emph>SE</emph> =.09, <emph>p</emph> =.01, <emph>df</emph> = 40), far-transfer, <emph>g</emph> = 0.26 (<emph>SE</emph> =.08, <emph>p</emph> =.003, <emph>df</emph> = 25). The largest gains for a near-transfer skill were found for WM, <emph>g</emph> = 0.37 (<emph>SE</emph> =.15, <emph>p</emph> =.03, <emph>df</emph> = 11).</p> <p>Separate WM analyses showed training gains for all of the following tasks; WM span backwards, <emph>g</emph> = 0.23 (<emph>SE</emph> = 0.04, <emph>p</emph> = 0.01, <emph>df</emph> = 7), WM span forwards, <emph>g</emph> = 0.19 (<emph>SE</emph> = 0.08, <emph>p</emph> =.05, <emph>df</emph> = 7), WM verbal, <emph>g</emph> = 0.50 (<emph>SE</emph> = 0.13, <emph>p</emph> =.006, <emph>df</emph> = 7) and WM visuospatial <emph>g</emph> = 0.38 (<emph>SE</emph> = 0.10, <emph>p</emph> =.003, <emph>df</emph> = 13). No statistically significant training gains were found for cognitive flexibility tasks, <emph>g</emph> = 0.28 (<emph>SE</emph> = 0.23, <emph>p</emph> =.25, <emph>df</emph> = 10), attention, <emph>g</emph> = 0.04 (<emph>SE</emph> = 0.33, <emph>p</emph> =.90, <emph>df</emph> = 9), inhibitory control, <emph>g</emph> = 0.11 (<emph>SE</emph> = 0.19, <emph>p</emph> =.57, <emph>df</emph> = 21), and general EF, <emph>g</emph> = 0.06 (<emph>SE</emph> = 0.23, <emph>p</emph> =.81, <emph>df</emph> = 7). Consequently, cognitive training was mostly effective for WM tasks in the near-transfer domain. In far-transfer effects, both numeracy and literacy, but not reasoning improved significantly with cognitive training interventions; literacy, <emph>g</emph> = 0.29 (<emph>SE</emph> = 0.06, <emph>p</emph> &lt;.001, <emph>df</emph> = 12), numeracy, <emph>g</emph> = 0.20 (<emph>SE</emph> = 0.07, <emph>p</emph> =.02, <emph>df</emph> = 13), reasoning, <emph>g</emph> = 0.28 (<emph>SE</emph> = 0.22, <emph>p</emph> =.24, <emph>df</emph> = 10).</p> <p>Next, we performed an analysis for different age groups with young (Ages 4–6), early primary- (Ages 7–9), and late primary-school (Ages 10-14) children. The results showed that cognitive training was more effective for younger, <emph>g</emph> =.27 (<emph>SE</emph> =.09; <emph>p</emph> =.005, <emph>CI</emph> [.08;.45]), <emph>df</emph> = 27, and early primary school-aged children, <emph>g</emph> =.18 (<emph>SE</emph> =.07; <emph>p</emph> =.02, CI [.03;.33]), <emph>df</emph> = 19, compared to late primary school-aged children, <emph>g</emph> =.19 (<emph>SE</emph> =.41; <emph>p</emph> =.65, <emph>CI</emph> [−.80;.1.19]), <emph>df</emph> = 6. It should be noted that we did not find many eligible studies that worked with older children, so that the results for older children must be interpreted cautiously.</p> <p>Average training duration was found to be significantly effective, <emph>g</emph> = 0.22, (<emph>SE</emph> = 0.08, <emph>p</emph> =.005, <emph>df</emph> = 48); high training time, <emph>g</emph> = 0.34, (<emph>SE</emph> =.20, <emph>p</emph> =.24, <emph>df</emph> = 2, <emph>CI</emph> [−.55;.1.24]). Here, the high effect size does not reach significance, the df is low, and the confidence interval shows some interventions worked well in high training time while others did not. Further, computer-based training was found to be the most effective training when compared to trainings using mobile and analogue techniques; computer, <emph>g</emph> = 0.26 (<emph>SE</emph> =.09, <emph>p</emph> =.005, <emph>df</emph> = 37), analogue, <emph>g</emph> = 0.20, (<emph>SE</emph> =.14, <emph>p</emph> = 0.19, <emph>df</emph> = 13), mobile, <emph>g</emph> = 0.06 (<emph>SE</emph> =.06, <emph>p</emph> =.39 <emph>df</emph> = 3). However, we were able to find four studies that used mobile interventions only; this is why our finding concerning such technology should be interpreted with caution. We further found high and significant effect size for non-adaptive training; non-adaptive, <emph>g</emph> = 0.29 (<emph>SE</emph> =.06, <emph>p</emph> &lt;.001, <emph>df</emph> = 19), adaptive, <emph>g</emph> = 0.20 (<emph>SE</emph> =.10, <emph>p</emph> =.06, <emph>df</emph> = 33). Both individual and group trainings were found to be effective test procedures for children; individual, <emph>g</emph> = 0.22 (<emph>SE</emph> =.08, <emph>p</emph> =.01, <emph>df</emph> = 43), group, <emph>g</emph> = 0.27 <emph>(SE</emph> =.09, <emph>p</emph> =.01, <emph>df</emph> = 10). Although there was a higher effect size in trainings with feedback; feedback, <emph>g</emph> = 0.28 (<emph>SE</emph> =.09, <emph>p</emph> =.003, df = 36), no feedback, <emph>g</emph> = 0.15 (<emph>SE</emph> =.13, <emph>p</emph> =.24, <emph>df</emph> = 16), this difference was not significant, <emph>p</emph> &gt;.05. When feedback was provided, verbal feedback had a higher and significant effect size, <emph>g</emph> = 0.36 (<emph>SE</emph> =.08, <emph>p</emph> &lt;.001, <emph>df</emph> = 10); visual, <emph>g</emph> = 0.22 (<emph>SE</emph> =.12, <emph>p</emph> =.07, <emph>df</emph> = 25), but no difference between these feedback types were found, <emph>p</emph> &gt;.05. The training was more effective when it was given at school <emph>g</emph> = 0.21 (<emph>SE</emph> =.06, <emph>p</emph> =.002, <emph>df</emph> = 38), but the number of studies that applied training at home (<emph>N</emph> = 4) or at the lab (<emph>N</emph> = 6) were too low to make final conclusions.</p> <hd id="AN0184162251-19">Age Differences in Moderator Analyses</hd> <p></p> <hd id="AN0184162251-20">Young Children</hd> <p>An in-depth analysis of the transfer effects of assessment outcomes separately for all age groups revealed that younger children (<emph>N<subs>studies</subs></emph> = 29) improved significantly in near-transfer tasks, but only marginally significantly in far-transfer tasks; near-transfer, <emph>g</emph> = 0.25 (<emph>SE</emph> =.08, <emph>p</emph> =.004, <emph>df</emph> = 22); far-transfer, <emph>g</emph> = 0.34 (<emph>SE</emph> =.15, <emph>p</emph> =.05, <emph>df</emph> = 11). The difference in gain between the two task types was not significant, <emph>p</emph> &gt;.05. Children in this age group showed the largest gains for WM tests, <emph>g</emph> = 0.45 (<emph>SE</emph> =.10, <emph>p</emph> =.003, <emph>df</emph> = 7), but no significant gains in any of the specific WM subtests. For WM visuospatial, a non-significant tendency for a gain with a larger effect size was found, <emph>g</emph> = 0.37 (<emph>SE</emph> =.18, <emph>p</emph> =.08, <emph>df</emph> = 8). Similarly, no significant training benefits were found for attention, <emph>g</emph> = 0.51 (<emph>SE</emph> =.37, <emph>p</emph> =.22, <emph>df</emph> = 5), CF, <emph>g</emph> = 0.24 (<emph>SE</emph> =.15, <emph>p</emph> =.17, <emph>df</emph> = 5), IC, <emph>g</emph> = -0.06 (<emph>SE</emph> =.20, <emph>p</emph> =.77, <emph>df</emph> = 13), and general EF, <emph>g</emph> = 0.29 (<emph>SE</emph> =.25, <emph>p</emph> =.33, <emph>df</emph> = 3).</p> <p>Both numeracy and literacy outcomes, but not reasoning, showed improvements in separate analyses; literacy, <emph>g</emph> = 0.40 (<emph>SE</emph> =.09, <emph>p</emph> =.01, <emph>df</emph> = 4), numeracy, <emph>g</emph> = 0.39 (<emph>SE</emph> =.08, <emph>p</emph> =.003, <emph>df</emph> = 5),reasoning, <emph>g</emph> = 0.33 (<emph>SE</emph> =.37, <emph>p</emph> =.41, <emph>df</emph> = 4). Younger children benefited significantly from cognitive training on average, but not studies with long training durations; average, <emph>g</emph> = 0.26 (<emph>SE</emph> =.09, <emph>p</emph> =.009, <emph>df</emph> = 24), long, <emph>g</emph> = 0.52 (<emph>SE</emph> =.17, <emph>p</emph> =.21, <emph>df</emph> = 1). For training tool, analogue training was found to be the most effective training for young children, <emph>g</emph> = 0.49 (<emph>SE</emph> =.11, <emph>p</emph> =.005, <emph>df</emph> = 6), while computer technology missed significance, <emph>g</emph> = 0.21 (<emph>SE</emph> =.11, <emph>p</emph> =.07, <emph>df</emph> = 18). Only two studies applied tablet training for young children which were not found to be significantly effective, <emph>g</emph> = 0.12 (<emph>SE</emph> =.07, <emph>p</emph> =.33, <emph>df</emph> = 1).</p> <p>Adaptive training did not seem to benefit younger children as much as non-adaptive training; non-adaptive, <emph>g</emph> = 0.32 (<emph>SE</emph> =.09, <emph>p</emph> =.007, <emph>df</emph> = 9); adaptive, <emph>g</emph> = 0.24 (<emph>SE</emph> =.12, <emph>p</emph> =.05, <emph>df</emph> = 17). Both, individual and group trainings were found to be effective training procedures for young children; individual, <emph>g</emph> = 0.26 (<emph>SE</emph> =.11, <emph>p</emph> =.03, <emph>df</emph> = 20), group, <emph>g</emph> = 0.31 (<emph>SE</emph> =.12, <emph>p</emph> =.04, <emph>df</emph> = 6). Young children mainly benefited from training with feedback; with, <emph>g</emph> = 0.28 (<emph>SE</emph> =.11, <emph>p</emph> =.02, <emph>df</emph> = 16); without, <emph>g</emph> = 0.27 (<emph>SE</emph> =.13, <emph>p</emph> =.06, <emph>df</emph> = 10). When feedback was provided, verbal feedback worked much better compared to visual feedback; verbal, <emph>g</emph> = 0.52 (<emph>SE</emph> =.06, <emph>p</emph> &lt;.0001, <emph>df</emph> = 5); visual, <emph>g</emph> = 0.14 (<emph>SE</emph> =.18, <emph>p</emph> =.46, <emph>df</emph> = 10).</p> <hd id="AN0184162251-21">Early Primary School-aged Children</hd> <p>For early school-aged children (<emph>N<subs>studies</subs></emph> = 19) significant training improvements were found only in far-transfer tasks, <emph>g</emph> = 0.21 (<emph>SE</emph> =.07, <emph>p</emph> =.01, <emph>df</emph> = 11). Near-transfer effect results were found to be not significant, <emph>g</emph> = 0.16 (<emph>SE</emph> =.10, <emph>p</emph> =.14, <emph>df</emph> = 10). The difference in gain between the two task types was not significant, <emph>p</emph> &lt;.05. In an in-depth analysis of separate tasks, cognitive training improved WM span forward (though the power is low), <emph>g</emph> = 0.41 (<emph>SE</emph> =.009, <emph>p</emph> =.01, <emph>df</emph> = 1) and literacy task outcomes significantly, <emph>g</emph> = 0.21 (<emph>SE</emph> =.08, <emph>p</emph> =.04, <emph>df</emph> = 7).</p> <p>Average-, but not long- training duration, was found to be effective; average training duration, <emph>g</emph> = 0.19 (<emph>SE</emph> =.08, <emph>p</emph> =.02, <emph>df</emph> = 15); long, <emph>g</emph> = -0.004 (<emph>SE</emph> =.000, <emph>p</emph> &lt;.0001, <emph>df</emph> = 1). The most effective training tool for early primary school-aged children was computers, <emph>g</emph> = 0.24 (<emph>SE</emph> =.09, <emph>p</emph> =.02, <emph>df</emph> = 13). Analogue training was not effective, however only two studies applied analogue training in this age group, <emph>g</emph> = 0.01 (<emph>SE</emph> =.01, <emph>p</emph> =.39, <emph>df</emph> = 1). There were only two studies that applied mobile technology, and they were not found to be effective, <emph>g</emph> = −0.032 (<emph>SE</emph> =.06, <emph>p</emph> =.67, <emph>df</emph> = 1).</p> <p>For both, adaptive and non-adaptive trainings no significant results were found; adaptive, <emph>g</emph> = 0.18 (<emph>SE</emph> =.08, <emph>p</emph> =.06, <emph>df</emph> = 11), non-adaptive, <emph>g</emph> = 0.20 (<emph>SE</emph> =.15, <emph>p</emph> =.27, <emph>df</emph> = 4). Individual training was more effective than group training, however, only two studies applied group training; individual, <emph>g</emph> = 0.21 (<emph>SE</emph> =.08, <emph>p</emph> =.02, <emph>df</emph> = 14), group, <emph>g</emph> = −0.01 (<emph>SE</emph> =.01, <emph>p</emph> =.39, <emph>df</emph> = 1). No feedback effect was found for this age group, <emph>p</emph> =.07.</p> <hd id="AN0184162251-22">Late Primary School-aged Children</hd> <p>We only found a few eligible studies with primary school-aged children (<emph>N<subs>studies</subs></emph> = 7). No significant near- or far-transfer training benefits were found; near-transfer, <emph>g</emph> = 0.27 (<emph>SE</emph> =.63, <emph>p</emph> =.69, <emph>df</emph> = 5), far-transfer, <emph>g</emph> = 0.09 (<emph>SE</emph> =.03, <emph>p</emph> =.18, <emph>df</emph> = 1). It is important to note that while the power was low for near-transfer benefits, the heterogeneity (<emph>SE</emph>) was found to be very high, indicating that findings varied greatly across studies. Separate task analyses revealed some significances and tendencies; however, the power was low for each task to make inferences (please see Supplemental Appendix C for details). No further significant effects of any moderator were found, <emph>p</emph> &gt;.05.</p> <hd id="AN0184162251-23">Discussion</hd> <p>Supporting children's EF development through cognitive training is a trending topic and an ongoing concern of developmental research. However, the best training tools and methods to establish and maintain good EF skills are still debated. In the current study, we investigated this issue by considering a wide range of relevant moderator characteristics that could potentially affect the success of cognitive training interventions.</p> <p>The findings of this meta-analysis show the importance of cognitive training especially in the early years of life with training benefits for both near- and far-transfer skills. Here, our moderator analyses stress the importance of age-appropriate cognitive training research designs and procedures. Children, in particular younger children, showed greater training gains from average compared to longer training session. While computer training was found to be the most effective technology when all ages are combined, analogue training benefited young children more while primary school children benefited more from computer training. The number of studies with applied mobile technology was too low to come to final conclusions on the effectiveness of mobile training techniques. Surprisingly, non-adaptive compared to adaptive training was more promising especially for younger children, and both, group and individual trainings were similarly effective training procedures. Young children benefited from training mainly when there was feedback, and if given, verbal feedback was found to be more effective. School training was the most effective training context. However, only a few studies conducted home or lab training.</p> <hd id="AN0184162251-24">Cognitive Training for Children in Different Age Groups</hd> <p>Cognitive training was effective for young and early primary school children. Greater effect sizes were found for younger compared to primary school children supporting both of our hypothesis that cognitive training contributes to children's outcomes in general and that early interventions seem to work better for young children ([<reflink idref="bib16" id="ref133">16</reflink>]; [<reflink idref="bib70" id="ref134">70</reflink>]; [<reflink idref="bib109" id="ref135">109</reflink>]). However, meta-regression analysis revealed no statistically significant differences in training effectiveness between younger and early primary school children (<emph>p</emph> &gt;.05), and younger and late-primary school children (<emph>p</emph> &gt;.05). Here, more research, in particular studies with primary school children are necessary.</p> <hd id="AN0184162251-25">Near- and Far-transfer Gains</hd> <p>We expected little or zero far-transfer gains when compared to near-transfer outcomes. However, when all age groups were combined (ages 4–14), significant training gains for both far- and near-transfer outcomes were found, which contrasts with previous research ([<reflink idref="bib70" id="ref136">70</reflink>]; [<reflink idref="bib84" id="ref137">84</reflink>]; [<reflink idref="bib90" id="ref138">90</reflink>]). However, it is important to note that (a) these studies mostly investigated the transfer effects of a single EF component (e.g., WM) instead of multiple EF components (e.g., general EFs, WM, IC, CF, etc.), that (b) various factors may influence EF performance outcomes such as intelligence, personality, emotions, etc. specifically for WM ([<reflink idref="bib13" id="ref139">13</reflink>]), and several studies in our meta-analysis applied WM task/s as their main training component (see Supplemental Appendix B), and that (c) there is still an ongoing and unresolved debate on the effects of near- and far-transfer benefits ([<reflink idref="bib57" id="ref140">57</reflink>]; [<reflink idref="bib90" id="ref141">90</reflink>]; [<reflink idref="bib93" id="ref142">93</reflink>]).</p> <p>Specifically, for separate age groups, our results revealed benefits for both types of transfer. Here, young children obtained the largest near-transfer benefits for WM outcomes. Concerning far-transfer effects, the largest benefits were found for literacy and numeracy. These results indicate that early interventions are very promising to improve both general cognitive and academic development ([<reflink idref="bib16" id="ref143">16</reflink>]). One reason for this could be that young children do not follow a specific strategy to learn a new content ([<reflink idref="bib102" id="ref144">102</reflink>]) and that during the developmental process either more cognitive resources are available for new learning content, or the contribution of different cognitive resources may vary across different age groups. For instance, children older than nine years may rely more on the phonological loop while performing WM required tasks ([<reflink idref="bib5" id="ref145">5</reflink>]), whereas young children may rely more on different WM resources (i.e., visuospatial) through analogous mental models ([<reflink idref="bib64" id="ref146">64</reflink>]; [<reflink idref="bib82" id="ref147">82</reflink>]).</p> <p>Near-transfer effects were not strong in older age groups. Cognitive training in early-primary school age seems to mainly support academic far-transfer skills ([<reflink idref="bib19" id="ref148">19</reflink>]) which is in-line with the systematic review of [<reflink idref="bib99" id="ref149">99</reflink>] who reported a link between the training of specific abilities such as EFs and academic skills (i.e., linguistic, numeracy and reasoning skills). However, other studies did not report such a link between cognitive training and far-transfer outcomes ([<reflink idref="bib90" id="ref150">90</reflink>], [<reflink idref="bib91" id="ref151">91</reflink>]). Based on the debates about far-transfer skills, [<reflink idref="bib44" id="ref152">44</reflink>] suggested to integrate cognitive training with situated learning theory ([<reflink idref="bib65" id="ref153">65</reflink>]) which is a learning paradigm based on learning in a contextual and authentic environment (e.g., at home, in a classroom) and learning by improving contextual conditions (e.g., improving learning methods) to facilitate the training gains for far-transfer skills.</p> <p>This is indeed a promising suggestion that brings home learning environment (HLE; [<reflink idref="bib76" id="ref154">76</reflink>]) and cognitive training together. Here, HLE offers a possibility for children to learn and be trained in their natural environment, and as the HLE is closely linked with academic and cognitive skills, the combined effect with cognitive training may even be stronger. While many cognitive training programs offer the possibility to be played at home (e.g., Cogmed), we did not come across many intervention studies that worked with children in the home settings especially with TD children (adults: [<reflink idref="bib14" id="ref155">14</reflink>]; very preterm children: [<reflink idref="bib117" id="ref156">117</reflink>]). Our analyses identified only a low number of studies conducted at home, a context for which more studies are needed.</p> <p>Contrary to our expectation, we did not find greater effects for most near-transfer skills ([<reflink idref="bib37" id="ref157">37</reflink>]; [<reflink idref="bib90" id="ref158">90</reflink>], [<reflink idref="bib91" id="ref159">91</reflink>]). Significant associations with WM span forward tasks were found in two studies for early primary school children. Even though WM span forwards and WM span backwards were reported to be associated with different cognitive processes in older children ([<reflink idref="bib45" id="ref160">45</reflink>]), our results do not support such association. Instead, a relatively high effect size (<emph>g</emph> =.27, <emph>SE</emph> =.63, <emph>p</emph> =.69, <emph>CI</emph> [−1.41 ;1.95], <emph>df</emph> = 5), with large heterogeneity (<emph>T</emph><sups>2</sups> = 95.08%) was found for training gains in near-transfer for late primary school-aged children. Here, large heterogeneity and low power indicates that more research is necessary to come to final conclusions. However, we still find this result interesting as the confidence interval indicates that some cognitive training interventions did not work well in this age group, whereas others were highly effective. We encourage researchers to investigate this issue further.</p> <hd id="AN0184162251-26">Training Duration</hd> <p>We expected greater gains for high training duration which may then lead to longer lasting effects ([<reflink idref="bib51" id="ref161">51</reflink>]). However, we found that young children benefited significantly from average-training durations. For high training durations, we found a relatively high looking effect size, however, it was not significant due to the heterogeneity across the three available studies, <emph>g</emph> = 0.34, (<emph>SE</emph> =.20, <emph>p</emph> =.24, <emph>df</emph> = 2).</p> <p>For early- and late-primary school children, average training duration was used more often in the studies. Our findings indicate that average training durations are promising to get better training outcomes. Long training durations may potentially be detrimental to the learning process for early primary school children, <emph>g</emph> = −0.004 (<emph>SE</emph> =.000, <emph>p</emph> &lt;.0001, <emph>df</emph> = 1). However, here due to low power, clearly more research is necessary.</p> <hd id="AN0184162251-27">Training Procedure</hd> <p>Previous cognitive and learning studies revealed the importance and the effect of adaptive training, in which children's responses were considered during the training program and the upcoming stimuli/displays were adjusted accordingly ([<reflink idref="bib35" id="ref162">35</reflink>]; [<reflink idref="bib48" id="ref163">48</reflink>]; [<reflink idref="bib121" id="ref164">121</reflink>]). Interestingly, we found non-adaptive cognitive training to be significantly effective. However, even though adaptive training procedures did not impact on child outcomes significantly (<emph>p</emph> =.06), the difference between non-adaptive and adaptive procedures was non-significant (<emph>p</emph> &gt;.05). High heterogeneity in adaptive studies (<emph>CI</emph> [−.009;.41]) shows that more research is needed with adaptive procedures.</p> <p>When all studies were analysed, computer training was found to be very effective when compared to analogue methods. However, specifically for young children, we found that analogue training worked much better (<emph>p</emph> =.005) than computer-based training, although computer-training was still associated with improved assessment outcomes (<emph>g</emph> =.21, <emph>p</emph> =.07). Here, one may consider the didactic design of analogue training which was provided by a trainer or a teacher who oversees training; a situation that usually is non-existent in computer-based studies. For primary school children, computer-based methods were effective, particularly for early primary school children (<emph>p</emph> =.02). Applying computer-based technology for late-primary school children resulted in high, but with non-significant effect sizes, <emph>g</emph> = 0.76 (<emph>SE</emph> =.65, <emph>p</emph> =.32, <emph>df</emph> = 3).</p> <p>In our meta-analyses, we tried to include as many articles as possible that applied mobile technology in their interventions given that this is a trending technological tool applied recently in child interventions ([<reflink idref="bib75" id="ref165">75</reflink>]). However, we were unable to identify many eligible studies. It is obvious that traditional and standardized computer-based methods are preferred and possibly more readily accessible for researchers. In addition, only a few child-friendly and effective training games or apps have been developed so far, and thus few have been applied in scientific interventions. Regarding our results with younger children, we still believe that interventions with mobile applications will have much to offer in the future (see also [<reflink idref="bib74" id="ref166">74</reflink>]).</p> <p>Both individual and group training procedures were effective, but significant differences were found for interventions with young children. While an individual training procedure was more effective for early primary school children, group training was not used often in school children. Younger children mainly benefited from training with task feedback, and when there was feedback, young children benefited from verbal feedback more when compared to visual feedback ([<reflink idref="bib101" id="ref167">101</reflink>]; [<reflink idref="bib100" id="ref168">100</reflink>]). Feedback did not facilitate training for older age groups ([<reflink idref="bib93" id="ref169">93</reflink>]). School was an effective training context, however, only a few training studies were conducted at home and at the lab to come to final conclusions about training context.</p> <hd id="AN0184162251-28">Limitations, Future prospects, and Conclusion</hd> <p>Some limitations mark this meta-analysis. For instance, not many studies were found that addressed our specific research focus and the relevant moderators, especially for late primary school children. Consequently, some of our findings need to be interpreted cautiously (for example if <emph>df</emph> &lt; 4). There is a possibility of increasing difference between groups and eventually decreasing the actual effect sizes. In particular, the surprising results concerning the near- and far-transfer effects both for the total number of studies and separately for each age group will add to the ongoing discussion rather than providing explanations and identifying potentially underlying mechanisms of training and transfer benefits ([<reflink idref="bib70" id="ref170">70</reflink>]; [<reflink idref="bib90" id="ref171">90</reflink>], [<reflink idref="bib91" id="ref172">91</reflink>], [<reflink idref="bib95" id="ref173">95</reflink>]). Further, we were not able to find many studies with mobile interventions and this left our research question concerning the training tool unanswered. In addition, it was difficult for some of the screened studies to identify age, training location, training duration or training procedures which may have led to less power and biased findings. For this reason, we recommend researchers to report detailed information clearly in their articles so that study replications, reviews, and meta-analyses are possible. Our study also did not investigate the potential effects of different didactic designs of analogue training. Teachers' and instructors' implementation of cognitive training methods may differ. The potential impact of different didactic designs should be investigated in future meta-analysis and/or systematic reviews.</p> <p>Most of the studies found within our criteria were from WEIRD countries (Western, educated, industrialized, rich, and democratic) which can be considered as a culturally limited sample. Consequently, the EF tasks here may not be valid across different cultural groups ([<reflink idref="bib38" id="ref174">38</reflink>]).</p> <p>Cognitive accessibility is getting increasingly important in recent years intending to benefit individuals with disabilities, as well as those from disadvantaged or diverse cultural backgrounds ([<reflink idref="bib58" id="ref175">58</reflink>]). We believe many families and their children may benefit from free/government funded, easy-to-understand, easy-to-use, and internationally recognised cognitive training programs. Such programs can be provided through group training in child centres/schools and through individual training with digital services that are easy to access. With such an approach, it may be possible to equally train children from different backgrounds.</p> <p>Long-term effects of cognitive training are still to be further investigated due to controversial results, especially for studies targeting children ([<reflink idref="bib54" id="ref176">54</reflink>]; Takacs &amp; Kassai, 2019; Sala &amp; Goblet, 2019). We invite researchers to further investigate the effects of cognitive training in a longitudinal study design and assess children's cognitive skills at different time points to understand at which point in time the benefits start to decay or for how long the benefits continue to exist.</p> <p>Our results indicate that cognitive training of EFs may benefit cognitive and academic skills of young children. Such training is particularly effective, if the training is given in a comprehensive fashion stimulating different components of EF. We agree with [<reflink idref="bib51" id="ref177">51</reflink>] that future studies should not solely focus on testing whether cognitive training is effective for children. Instead, researchers should take potential moderating factors into account while designing their studies. For example, age differences (as in our study) suggest that not every training design or procedure will be effective for every age group, and training should be adjusted accordingly ([<reflink idref="bib41" id="ref178">41</reflink>]; [<reflink idref="bib85" id="ref179">85</reflink>]; [<reflink idref="bib92" id="ref180">92</reflink>]). Executive functions can be influenced by various environmental and individual factors such as poverty/low income of the family ([<reflink idref="bib33" id="ref181">33</reflink>]; [<reflink idref="bib77" id="ref182">77</reflink>]), cultural background ([<reflink idref="bib89" id="ref183">89</reflink>]), and the pedagogical context during cognitive training (lab vs. school vs. home context). Consequently, environmental (e.g., family background variables such as low-socioeconomic status or cultural influences), and individual factors such as potential memory capacity differences across age groups ([<reflink idref="bib46" id="ref184">46</reflink>]) should be considered in future cognitive training studies.</p> <hd id="AN0184162251-29">Supplemental Material</hd> <p>Graph: Supplemental material, sj-pdf-1-sgo-10.1177_21582440241311060 for Training of Executive Functions in Children: A meta-analysis of cognitive training interventions by Efsun Birtwistle, Olga Chernikova, Miriam Wünsch and Frank Niklas in SAGE Open</p> <hd id="AN0184162251-30">Supplemental Material</hd> <p>Graph: Supplemental material, sj-pdf-2-sgo-10.1177_21582440241311060 for Training of Executive Functions in Children: A meta-analysis of cognitive training interventions by Efsun Birtwistle, Olga Chernikova, Miriam Wünsch and Frank Niklas in SAGE Open</p> <hd id="AN0184162251-31">Supplemental Material</hd> <p>Graph: Supplemental material, sj-pdf-3-sgo-10.1177_21582440241311060 for Training of Executive Functions in Children: A meta-analysis of cognitive training interventions by Efsun Birtwistle, Olga Chernikova, Miriam Wünsch and Frank Niklas in SAGE Open</p> <p>We would like to thank all project seminar students (year 2020; e.g., Adrian Faruga, Amelie Grabmaier, Ke Sun, Ownkyeong Lee, Stephanie Piehlmeier) for their contribution during literature search and abstract screening. 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Developmental Science, 21(1), 12511. https://doi.org/10.1111/desc.12511</bibtext> </blist> </ref> <ref id="AN0184162251-33"> <title> Footnotes </title> <blist> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research has been conducted as part of the Learning4Kids project. Frank Niklas has received funding for Learning4Kids from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement ERC StG no 801980). The sponsors or funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.</bibtext> </blist> <blist> <bibtext> Efsun Birtwistle</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0002-6958-5882 Olga Chernikova</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0002-0112-3665 Miriam Wünsch</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0009-0009-5019-9576 Frank Niklas</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-3777-7388</bibtext> </blist> <blist> <bibtext> The research data associated with the current study will be made available on request.</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By Efsun Birtwistle; Olga Chernikova; Miriam Wünsch and Frank 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| Header | DbId: eric DbLabel: ERIC An: EJ1466942 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Training of Executive Functions in Children: A Meta-Analysis of Cognitive Training Interventions – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Efsun+Birtwistle%22">Efsun Birtwistle</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6958-5882">0000-0002-6958-5882</externalLink>)<br /><searchLink fieldCode="AR" term="%22Olga+Chernikova%22">Olga Chernikova</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0112-3665">0000-0002-0112-3665</externalLink>)<br /><searchLink fieldCode="AR" term="%22Miriam+Wünsch%22">Miriam Wünsch</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0009-5019-9576">0009-0009-5019-9576</externalLink>)<br /><searchLink fieldCode="AR" term="%22Frank+Niklas%22">Frank Niklas</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3777-7388">0000-0002-3777-7388</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22SAGE+Open%22"><i>SAGE Open</i></searchLink>. 2025 15(1). – Name: Avail Label: Availability Group: Avail Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 18 – Name: DatePubCY Label: Publication Date Group: Date Data: 2025 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Information Analyses – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Junior+High+Schools%22">Junior High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Middle+Schools%22">Middle Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Preschool+Children%22">Preschool Children</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+School+Students%22">Elementary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Middle+School+Students%22">Middle School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Executive+Function%22">Executive Function</searchLink><br /><searchLink fieldCode="DE" term="%22Short+Term+Memory%22">Short Term Memory</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Development%22">Cognitive Development</searchLink><br /><searchLink fieldCode="DE" term="%22Numeracy%22">Numeracy</searchLink><br /><searchLink fieldCode="DE" term="%22Literacy%22">Literacy</searchLink><br /><searchLink fieldCode="DE" term="%22Age+Differences%22">Age Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Age+Groups%22">Age Groups</searchLink><br /><searchLink fieldCode="DE" term="%22Child+Development%22">Child Development</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Development%22">Student Development</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Instruction%22">Computer Assisted Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Handheld+Devices%22">Handheld Devices</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Early+Intervention%22">Early Intervention</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1177/21582440241311060 – Name: ISSN Label: ISSN Group: ISSN Data: 2158-2440 – Name: Abstract Label: Abstract Group: Ab Data: We investigated the effect of cognitive training of executive functions on children's cognitive outcomes. To address this issue, a systematic meta-analysis of published research articles on cognitive training interventions was performed considering children's age, training duration, -procedure, and -technology in moderator analyses. The results (N = 57) of a random-effects-model showed that cognitive training was effective with a total effect size of g = 23. The training was more effective for younger compared to older children. Training benefits were found for near- and far-transfer tasks. The largest gains for a near-transfer skill were found for working memory. Both numeracy and literacy skills profited from training. Computer training was very effective, however, only a few studies used mobile technology. Non-adaptive training was associated with greater effect sizes and both group and individual training were similarly effective. Verbal feedback was important for younger children. School was an effective context for training, however, only a few studies were conducted at home or at the lab. The findings are discussed and advocate an early start of cognitive training interventions. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2025 – Name: AN Label: Accession Number Group: ID Data: EJ1466942 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1466942 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/21582440241311060 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 18 Subjects: – SubjectFull: Preschool Children Type: general – SubjectFull: Elementary School Students Type: general – SubjectFull: Middle School Students Type: general – SubjectFull: Executive Function Type: general – SubjectFull: Short Term Memory Type: general – SubjectFull: Cognitive Development Type: general – SubjectFull: Numeracy Type: general – SubjectFull: Literacy Type: general – SubjectFull: Age Differences Type: general – SubjectFull: Age Groups Type: general – SubjectFull: Child Development Type: general – SubjectFull: Student Development Type: general – SubjectFull: Computer Assisted Instruction Type: general – SubjectFull: Handheld Devices Type: general – SubjectFull: Feedback (Response) Type: general – SubjectFull: Early Intervention Type: general Titles: – TitleFull: Training of Executive Functions in Children: A Meta-Analysis of Cognitive Training Interventions Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Efsun Birtwistle – PersonEntity: Name: NameFull: Olga Chernikova – PersonEntity: Name: NameFull: Miriam Wünsch – PersonEntity: Name: NameFull: Frank Niklas IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-electronic Value: 2158-2440 Numbering: – Type: volume Value: 15 – Type: issue Value: 1 Titles: – TitleFull: SAGE Open Type: main |
| ResultId | 1 |
