The Effectiveness of Attention Training in Specific Learning Disorders:A Systematic Review and Transfer Analysis

The Effectiveness of Attention Training in Specific Learning Disorders:A Systematic Review and Transfer Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Effectiveness of Attention Training in Specific Learning Disorders:A Systematic Review and Transfer Analysis Vahid Nejati, Fatemeh Ghafouri, Maedeh Hejazi, Roozbeh Behroozmand This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7853101/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Individuals with Specific Learning Disabilities (SLD) experience cognitive impairments, including difficulties with attention. As attention constitutes a fundamental cognitive function that is trainable, its foundational nature makes it a suitable target for interventions aiming to extend training effects across other cognitive domains. This study systematically explores the transferability of attention training in individuals with SLD, guided by the FIELD framework of transfer, which stands for Function, Implement, Ecology, Level, and Durability of effects. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 31 original experiments were included in this study. Our transfer analysis, structured as a conceptual meta-analysis rooted in assessing effect sizes, sheds light on the pivotal influence of various factors, including age, SLD subtype, intervention dose, level, setting, and material. The findings indicate a heightened transfer effect in younger participants, those with combined SLD, interventions of extended durations, and those employing mental materials, in contrast to paper-based and computerized interventions. Additionally, attention state training exhibited superior transfer effects when compared to attention process training. These findings emphasize the crucial role of collaborative efforts among researchers, educators, and therapists for crafting targeted interventions that adeptly cater to the diverse needs of individuals with learning disabilities. Health sciences/Health care Biological sciences/Psychology Social science/Psychology Specific Learning Disabilities (SLD) FIELD’s model of transfer attention training systematic review Figures Figure 1 Introduction Specific Learning Disabilities (SLD) are characterized by persistent and significant difficulties in acquiring and effectively utilizing skills related to reading (dyslexia), writing (dysgraphia), or mathematics (dyscalculia). These challenges persist despite individuals receiving conventional instruction, possessing intact sensory and motor functioning, normal intelligence, proper motivation, and sufficient sociocultural opportunities (APA, 2013 ). In the definition of SLD, the impairment is attributed to central information processing, cognitive functions, rather than the initial incoming sensory information or the later outgoing motor functions. A variety of cognitive impairment have been described in individuals with SLD including impaired perception (Yazdani et al., 2021 ), attention, inhibitory control (Szucs et al., 2013 ), and working memory (Maehler & Schuchardt, 2016 ; Schuchardt et al., 2008 ), and cognitive flexibility (Cartwright et al., 2017 ). Attention plays a pivotal role in cognitive functions, serving as a crucial element for selecting relevant information for perception, inhibiting irrelevant information, maintaining information in working memory for subsequent processing, and facilitating the seamless shift between different pieces of information to enhance cognitive flexibility (Nejati, 2021 ). At the neural level, dyslexia, dysgraphia, and dyscalculia are each linked to specific neural substrates. Dyslexia is commonly associated with dysfunction in the left temporoparietal regions and the visual word form area, which are critical for reading processes. Dysgraphia has been connected to altered activity in premotor and parietal areas involved in writing and motor planning. Dyscalculia is related to atypical functioning of the intraparietal sulcus, a region essential for numerical processing and quantity representation (Saini et al., 2023 ). These structures could be considered as neural correlates of attention. The visual word form area is part of both language and attention circuitry (Chen et al., 2019 ). It shows strong intrinsic connectivity with fronto-parietal networks implicated in attentional control. Furthermore, its connection with the dorsal fronto-parietal attention network has been found to predict visuospatial attention (Chen et al., 2019 ). Similarly, the premotor cortex, receiving input from the posterior parietal cortex, is involved in spatial orientation and primarily regulates proximal motor functions (Abe & Hanakawa, 2009 ). The intraparietal sulcus is consistently activated in tasks requiring selective attention, particularly when individuals must filter target stimuli from competing distractors (Brown et al., 2023 ). Furthermore, when it comes to symptoms of SLD, difficulties in reading, writing, or calculation may often be linked to underlying attentional impairments. Attention plays a foundational role in the functions that are essential for academic skill acquisition across domains. In reading, attention supports multiple stages, including word recognition, fluency, and comprehension. Sustained attention facilitates the acquisition of grapheme-phoneme correspondences and the decoding of unfamiliar words (Macdonald et al., 2021 ). Attentional control is also critical for effective phonological processing, enabling learners to shift between phonological representations and suppress irrelevant stimuli (Kibby et al., 2014 ). For reading comprehension, attentional mechanisms, along with working memory, allow readers to maintain textual information, integrate meaning across sentences, and selectively focus on relevant content (Dehn, 2011 ). In writing, attention is implicated in a range of components, including handwriting, spelling, vocabulary use, and overall written expression (Reid et al., 2023 ). Prior research, particularly in individuals with ADHD, has shown associations between attention deficits and poor writing performance, shorter text with more errors (Graham et al., 2016 ; Re et al., 2007 ). Moreover, writing is supported by a broader network of cognitive processes—including short-term memory, working memory, and executive functions, that are sensitive to attentional disruption (Cheng et al., 2022 ). Similarly, mathematical learning relies heavily on attentional control. Solving multi-step problems requires the ability to sustain focus, inhibit distractions, and manage sequential operations (Commodari & Di Blasi, 2014 ). Teacher-rated inattention has been shown to explain unique variance in children's math performance, independent of other cognitive factors (Cirino et al., 2007 ; Fuchs et al., 2005 ). Notably, attention has emerged as a stronger predictor of arithmetic skills and problem-solving accuracy than working memory (Fuchs et al., 2005 ). Sustained attention, in particular, is closely linked to arithmetic achievement (Orbach & Fritz, 2022 ), suggesting that attentional impairments may hinder the acquisition and execution of mathematical procedures (Calub et al., 2019 ). Altogether, these findings suggest that attentional functioning is not merely a co-occurring difficulty in SLD, but may play a mechanistic role in the emergence and persistence of academic deficits. Targeting attention in intervention design may therefore serve as a foundational strategy for enhancing learning outcomes across domains. The central role of cognitive function in SLD makes cognitive training a potential intervention candidate. Cognitive training is defined as "the process of remediation or compensation for cognitive deficits and related outcomes through well-established programs by the therapist" (Nejati, 2022 ). While cognitive training can address various cognitive impairments, attention, as a fundamental cognitive function, holds particular significance for training. Two prevalent attention training interventions include Attentional State Training (AST) and Attentional Process Training (APT). AST focuses on teaching individuals to regulate their attentional state and arousal levels through techniques like breathing exercises, meditation, and relaxation methods. On the other hand, APT concentrates on training specific attentional processes by guiding individuals through progressive tasks that involve selective, sustained, shifting, and divided attention (Posner et al., 2015 ; Tang & Posner, 2009 ). Several studies used AST ( Alqarni & Hammad, 2021 ; Beauchemin et al., 2008 ; Jones & Finch, 2020 ; Veysi et al., 2015 ; Bertoni et al., 2019 ; Malboeuf-Hurtubise et al., 2017 ; Malboeuf-Hurtubise et al., 2018 ; Malboeuf-Hurtubise et al., 2019 ) and APT (Ashkenazi & Henik, 2012 ; Azizi et al., 2018 ; Bertoni et al., 2019 ; Bertoni et al., 2021 ; Caldani et al., 2020 ; Chenault et al., 2006 ; Facoetti et al., 2003 ; Flores-Gallegos et al., 2022 ; Flores-Gallegos et al., 2022 ; Franceschini et al., 2013 ; Franceschini et al., 2017 ; Gibert et al., 2023 ; Guarnera & D’Amico, 2014 ; Pérez-Puelles et al., 2022 ; Heim et al., 2015 ; Helland et al., 2018 ; Lorusso et al., 2005 ; Lorusso et al., 2006 ; Pedroli et al., 2017 ; Peters et al., 2021 ; Zhao et al., 2019 ; Ren, 2023 ) for training of individuals with SLD. While acknowledging the significance of attention as a fundamental cognitive process in the management of individuals with SLD, the primary challenge within this group pertains to academic performance at the behavioral level. Certainly, when cognitive training shows positive efficacy in enhancing the targeted cognitive function but fails to translate into improved academic performance in SLD, its overall effectiveness comes into question. Certainly, the focus of specific interventions should extend beyond test results within the same specific domain and be directed towards addressing behavioral symptoms. The transmission of training effects from a trained domain to untrained domains referred to as transfer (Haskell, 2001). Transferability serves as a crucial metric for evaluation of intervention effectiveness, determining whether an intervention targeting the core pathology can bring about improvements that span all affected domains. In the context of the FIELD model, transfer encompasses five essential dimensions: Function, Implement, Ecology, Level, and Durability (Nejati, 2024 ). "Function" pertains to the ability to transfer performance from a trained function, such as imitation, to an untrained function, such as theory of mind. The "Implement" dimension involves various materials and methods used in the assessment and training process, Table 1 . Demonstrating improvement through diverse assessment tools, rather than intervention materials and methods, signifies implement transfer. "Ecological transfer" entails transferring intervention effects from a specific setting, such as a clinical environment, to an entirely different and new setting, like the home. "Level transfer" focuses on transferring training effects across different levels, including neural, cognitive, and behavioral levels. Lastly, "Durability" examines the duration of the training effects and how long they persist after discharge. Table 1 Description of different transfer domains based on FIELD model Dimension Description Function The transfer of training effect from trained function to an untrained function(s). Implement Improvement demonstrated through diverse assessment tools during the assessment and training process. Ecology The transference of intervention effects from one specific setting (e.g., clinic) to an entirely different and new setting (e.g. home). Level The transfer of training effects across different levels, including neural, cognitive, and behavioral levels. Duration Concerned with how long the effects of training persist after discharge. Despite its critical importance, the issue of transfer has remained a topic of ongoing debate in the cognitive training literature (Greenwood & Parasuraman, 2016; Smid et al., 2020; Westwood et al., 2023). While numerous studies have reported improvements in trained cognitive tasks, evidence for transfer, particularly transfer to academic performance and everyday functioning, remains inconsistent. This inconsistency raises important concerns regarding the ecological validity and long-term utility of cognitive training interventions (Noack et al., 2014). In many cases, training gains are limited to task-specific improvements, with minimal evidence of meaningful changes in real-life skills or academic achievement (Westwood et al., 2023). One major challenge in this field is the absence of a unified definition and standardized criteria for evaluating transfer. Some studies operationalize transfer narrowly—such as improvements in tasks that are structurally similar but not identical to the trained tasks—while others use broader behavioral or academic outcomes, leading to considerable heterogeneity in findings. Additionally, methodological variability across studies, such as differences in training duration, outcome measures, population characteristics, and the presence or absence of control conditions, further complicates the interpretation of results. By adopting the FIELD model, the current study seeks to contribute to this ongoing discourse by offering a more structured and multidimensional approach to conceptualizing transfer. Rather than relying solely on task-based outcomes or surface-level performance shifts, the FIELD model allows for a comprehensive evaluation of how training effects generalize across functions, materials, contexts, hierarchical levels, and over time. This framework not only provides a conceptual scaffold for organizing existing evidence but also guides future research in systematically assessing the real-world relevance and sustainability of cognitive training effects. In the current study, our objective was to investigate the transferability of attention training in individuals with SLD across diverse domains, using the FIELD model. Additionally, we aimed to identify significant determinants influencing the transferability of attention training. Methodology The methodology for this systematic review is outlined below and adheres to the guidelines set forth by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The review protocol has been registered on the PROSPERO website ( www.crd.york.ac.uk/PROSPERO ) under registration number CRD42023462407. Study Selection Criteria. Type of Studies. This systematic review encompasses diverse study designs. The focus is on interventions involving attention training. Exclusion criteria encompass reviews, newspapers, book chapters, notes, surveys, letters to the editor, case reports, and case series. No restrictions are imposed on language or geographic location. All studies involving individuals diagnosed with specific learning disorders are considered. There are no limitations regarding age, gender, or nationality. Specific learning disorders may include reading impairment (dyslexia), impairment in written expression, and impairment in mathematics (dyscalculia), Fig. 1 . Search Strategy. A comprehensive search was conducted across multiple databases, including PubMed, Scopus, Web of Science, Eric, APA PsycINFO (via EBSCO), and Cochrane Central Register of Controlled Trials (CENTRAL). The search strategy incorporated terms related to attentional training and learning disability, with adaptations made for each specific database. For instance, the search syntax used for PubMed was as follows: ("attention rehabilitation"[tiab] OR "attention training"[tiab] OR "attentional enhancement"[tiab] OR "attentional intervention"[tiab] OR mindfulness[tiab] OR yoga[tiab]) AND (Dyscalculia[tiab] OR "mathematics disorder"[tiab] OR Dyslexia[tiab] OR "reading disorder"[tiab] OR "reading disability"[tiab] OR Dysgraphia[tiab] OR "writing disorder"[tiab] OR "specific learning disorder"[tiab] OR "learning disability"[tiab]). The study selection process is visually represented in a flowchart based on PRISMA guidelines. The full search strategy for databases is provided in the supplementary material. Additionally, bibliographies of relevant prior reviews and primary studies identified by the search strategy were scrutinized for additional pertinent papers. No restrictions were applied based on language or geography. Searches were conducted from the beginning of 1990 to the end of June 2023. Data Extraction. Search results were imported into Mendeley software to identify and eliminate duplicate records. Subsequently, one researcher (MH) screened titles and abstracts based on predetermined eligibility criteria. Following this, two independent researchers (MH and FG) conducted a full-text screening of eligible studies, resolving discrepancies through discussion or consultation with a third researcher (VN), if needed. Data extraction from primary articles was independently performed by two reviewers (FG, MH) using a predefined form. Any discrepancies were resolved through consensus between the two reviewers, with a third reviewer (VN) acting as an arbitrator when necessary. Extracted data included the first author's name, publication year, study country, design, intervention type, sample size, participant characteristics (gender, age, type of SLD), blinding, and various outcomes (behavioral, cognitive, neural). Risk of Bias. Risk of bias assessments were conducted using the Cochrane extension tool, categorizing studies into low, high, or unclear risk of bias. Two investigators (FGH, MH) independently rated each study, resolving differences through consensus or consulting a third investigator (VN). Transfer Measurement and Analysis. In accordance with the FIELD model of transfer, each assessment test is assigned a value within the FIELD framework. The effect size of the intervention FIELD is subsequently juxtaposed with the effect size of the assessment FIELD, serving as the gauge for transfer. To illustrate, let's consider the Continuous Performance Test (CPT), measuring sustained attention (F). Administered through a computer as an objective test (I), conducted in a clinical setting (E), and serving as a cognitive measure (L), the CPT is administered across three sessions—pre-intervention, post-intervention, and follow-up (D). Additionally, we consider an objective computerized attention training in a clinical setting. Any disparities observed between the FIELD domains signify a transfer effect. Consequently, the effect size of the respective test is deemed the measure of transfer in this study. The transfer measure within each FIELD’s domain is computed by summing up the effect sizes of the corresponding assessments. Table 3 illustrates the transfer FIELD for both assessments and interventions across all studies, with the effect sizes of the associated tests documented in Appendix A for reference. To assess side effects across different studies, diverse formulas were applied. In specific scenarios, conducting an analysis based on changes from baseline proves to be more efficient and potent than comparing final values. This approach eliminates a portion of between-person variability from the analysis, yielding more precise and robust results. By centering on changes within individuals over time, the impact of individual differences is minimized, enabling a more sensitive detection of treatment effects or interventions. Consequently, analyzing changes from baseline offers a clearer portrayal of the true treatment effect, augmenting the statistical power of the study (Cumpston et al., 2019). To compute the mean change in each group, the post-intervention mean is subtracted from the baseline mean. The standard deviation of the change from baseline for the experimental/control intervention was imputed using the following formula: The comparison of effect sizes across diverse studies and field domains involved using Stata (Version 14.2). This software was employed to weigh the effect sizes based on both the sample size and the number of tests conducted in each study. By integrating sample size and test quantity, the analysis took into consideration the variability and reliability of effect size estimates across studies. The weighting of effect sizes facilitated giving more weight to studies with larger sample sizes and more robust findings, while downweighing studies with smaller sample sizes or potentially less reliable results. Stata was utilized to execute transfer analysis procedures, considering study-specific weights. The software generated pooled effect sizes, offering a more accurate representation of the overall effect across the field domains. This approach allowed for a thorough comparison of effect sizes from different studies, enhancing the assessment of the combined effect. In this study, the interpretation of effect sizes utilized Hedges' g (Standardized Mean Difference for small sample sizes), with benchmarks categorizing small, medium, and large effect sizes set at approximately 0.2, 0.5, and 0.8, respectively. These benchmarks enabled a consistent and meaningful comparison of effect sizes across studies, facilitating the evaluation of the practical significance and impact of observed effects. The use of Hedges' g and standardized benchmarks provided valuable insights into the effectiveness and relevance of the interventions or treatments under investigation, enhancing the overall interpretability of the results. Results A total of 31 studies were included in this review, encompassing a participant pool of 887 individuals diagnosed with SLD. Among the participants, there were 804 children, 74 adolescents and 9 adults. The gender distribution comprised 259 females, 404 males, and 224 cases where gender was not specified. Subtypes of SLD were identified in 20 studies, with 504 participants classified with dyslexia, 23 with dyscalculia, and 360 with combined subtype conditoins. The SLD diagnosis was based on various criteria, including DSM-4 in 2 studies, DSM-5 in 4 studies, ICD 10 in 2 studies, QRS-L in 1 study, ABCA in 1 study, and not specified in 21 studies. Study designs included 14 randomized clinical trials (RCTs) and 17 non-randomized controlled (NRC) studies. Among the included studies, 13 employed an active control group, while one remaining study (Flores-Gallegos et al., 2022) used waitlist controls. Active control interventions varied and included Neurofeedback (Azizi et al., 2018), non-action video game (Bertoni et al., 2019; Franceschini et al., 2013, 2017), visual training (C Gibert et al., 2023), reading training (Chenault et al., 2006; Lorusso et al., 2005, 2006), non-attentional training (Ren et al., 2023), and social skill training (Malboeuf-Hurtubise et al., 2019). The sample sizes in the included studies ranged from 9 to 64 participants (see Table 2 for details). Tables 3 and 4 outline the assessment tools used in the studies. Assessment procedures included two sessions (pre-test and post-test) in 26 studies and three sessions (pre-test, post-test, and follow-up) in 5 studies. Follow-up periods ranged from 2 months to 8 months. Assessments were categorized into neural, cognitive, and behavioral levels, as presented in Tables 3 and 4. Neural assessments, exemplified by fMRI (functional magnetic resonance imaging) or electroencephalography, employed physiological measurements to assess neural activity. Cognitive assessments, such as the continuous performance test, entailed tasks designed to gauge the accuracy and/or speed of cognitive performance. Behavioral assessments utilized objective measures in the form of questionnaire-based evaluations to appraise behavioral performance. The evaluations in the studies included were conducted in diverse settings, including school (11 studies) and clinical settings (14 studies). Some studies employed evaluations in multiple settings, such as school and clinic (1 study). Concerning interventions, they were implemented in various settings as well, including School (5 studies) and clinical settings (11 studies). Similar to assessments, some studies executed interventions in multiple settings, such as home and clinic (1 study), and school and home (6 studies). In Tables 3 and 4, the behavioral and cognitive assessment tools are categorized based on the agent/responder (self-, parent-, teacher-, clinician-rating), objectivity, material (paper and pencil or computerized tasks), and the setting in which they were performed (clinic, home, or school). Table 2. Demographic and diagnostic characteristics of studies Authors, Year Study Design, Control 1 N (I, C), Age Mean (SD), Age Range, Gender (F:M), Education (Yr) Diagnosis 2 Subtypes DD, MLD 3 Alqarni & Hammad, 2021 CG, P, OL 30 (15, 15), ns (ns), ns, 0:30, ns ns ns Ashkenazi & Henik, 2012 SG, OL 9 (9, 0), 24.7 (1.98), ns, 7:2, ns ns 0, 9 Azizi et al., 2018 CG, P, OL 45 (15, 15), 8.53 (1.62), 7-10, 30:15, 3 DSM5 ns Beauchemin et al., 2008 SG, OL 34 (34, 0), 16.61 (ns), 13-18, 5:29, ns ns ns Bertoni et al., 2019 CG, P, OL 14 (7, 7), 10.1 (1.6), ns, 6:8, ns DSM5 14, 0 Bertoni et al., 2021 CO, OL 14 (7, 7), 8.93 (.99), ns, 4:10, ns DSM5 ns Caldani et al., 2020 CG, P, SB 50 (25, 25), 9.65 (.35), 7.8-12, ns, ns ns 50, 0 Chenault et al., 2006 CG, P, OL 20 (10, 10), 10.66 (.87), ns, 8:12, 5 ns 20, 0 Facoetti et al., 2003 P, OL 24 (12, 12), 9.48 (ns), 7-9, 4:20, ns ns 24, 0 Flores-Gallegos et al., 2022 CG, OL 11 (6, 5), 7.9 (ns), 6.9-10.9, 4:7, ns ns ns Franceschini et al., 2013 CG, SB 20 (10, 10), 9.8 (1.4), 7-13, ns, ns ns 20, 0 Franceschini et al., 2017 CG, P, OL 28 (16, 12), 10.27 (1.72), 7.8-14.3, 8:20, ns ns 28, 0 Gibert et al., 2023 CG, P, OL 39 (24, 15), 9.04 (.11), ns, 18:21, ns ns 39, 0 Guarnera & D’Amico, 2014 CG, P, OL 14 (7, 7), 9.42 (.4), 8-10, 6:8, ns ABCA 0, 14 Pérez-Puelles et al., 2022 SG, OL 32 (32,0), 8.97 (1.61), 6-12, 9:23, ns QRS-L ns Heim et al., 2015 CG, P, OL 33 (7, 26), 10 (0.6), 8.7-11.2, ns, ns ns 33, 0 Helland et al., 2018 SG, OL 16 (16, 31), 8.78 (.26), 8-9, 6:10, 2 ns 16, 0 Jones & Finch, 2020 SG, OL 9 (9,0), ns (ns), ns, ns, ns ns ns Lorusso et al., 2005 CG, P, OL 12 (6, 6), 10.75 (2.46), 8-14, ns, ns ICD-10 12, 0 Lorusso et al., 2006 CG, P, OL 25 (14, 11), 9.8 (2.24), 7-15, 3:22, ns ICD-10 25, 0 Malboeuf-Hurtubise et al., 2017 SG, OL 14 (14, 0), 10.7 (1.1), 9-12, 8:6, ns ns ns Malboeuf-Hurtubise et al., 2018 SG, OL 14 (14, 0), 10.7 (1.1), 9-12, 8:6, ns ns ns Malboeuf-Hurtubise et al., 2019 CG, P, OL 23 (13, 10), ns (ns), 9-12, ns, ns ns ns Meng et al., 2014 SG, P, OL 18 (18, 0), 10.34 (.78), ns, 10:26, 5 DSM-IV 18, 0 Pedroli et al., 2017 SG, OL 10 (10, 0), 10.6 (1.4), 9-12, 2:8, 5 5.5 ns 10, 0 Peters et al., 2021 CG, P, SB 64 (23, 22, 19), 10.53 (.99), 8-13, ns, 4.5 DSM5 64, 0 Ren, 2023 CG, P, DB 45 (30, 15), 10.37 (1.31), ns, 9:36, 4.5 ns 45,0 Solan et al., 2003 CG, P, OL 30 (15, 15), 11.3(.3), ns, ns, 6 ns 30, 0 Solan et al., 2004 SG, OL 16 (16, 0), ns (ns), ns, 7 ns 16, 0 Veysi et al., 2015 CG, P, OL 40 (20, 20), 13.85 (2.23), ns, 40:0, ns DSM-IV ns Zhao et al., 2019 CG, P, OL 40 (20, 20), 10.06 (1.5), ns, 11:29, 4 ns 40, 0 Abbreviations: 1. CG: Control Group, CO: Crossover, DB: Double Blind, OL: Open Label, P: Parallel, SB: Single Blind, SG: Single Group, 3. DD: Developmental Dyslexia, MLD: Mathematical Learning Disorder, 2. ADCA: abilità di calcolo aritmetico, QRS-L: Questionnaire of Risk Signs and Learning Problems The interventions administered in the studies covered both the behavioral aspect, specifically attentional state training (n=7), and the cognitive aspect, concentrating on attentional process training (n=24). The duration of these intervention programs varied among studies, with attentional state training spanning from 4.16 to 24 hours in 7 studies, and attentional processing training ranging from 0.17 to 24 hours in 24 studies (refer to Table 6 for details). Table 5 provides a description and categorization of intervention program properties based on administration (clinician-, parent-, teacher-, or self-administered), objectivity (objective task-based intervention or subjective intervention), and material (computerized, paper and pencil, educational, or motoric). These characteristics of assessment tools and intervention programs were employed in our framework to explore various domains of transfer. The findings of the studies included in the analysis were evaluated within the framework of five transfer domains. In the realm of function, behavioral and cognitive interventions were interpreted differently. In the context of behavioral intervention, AST, the transference of training effects to SLD symptoms was categorized as near transfer. Conversely, the extension of training effects to broader performance aspects outside the intervention's focus, such as anxiety, was deemed as far transfer. Additionally, improvements in mindfulness or attentional states were noted as having no discernible transfer. Within the scope of cognitive intervention, APT, the categorization of near and far transfer hinged on the functional likelihood of cognitive functions. Attention, as a foundational function with diverse domains, demonstrated no transfer effect when targeted for enhancement across various attentional levels, such as focused, sustained, selective, shifting, and divided attention. The transfer of training effects to executive functions, encompassing working memory, inhibitory control, and cognitive flexibility, was considered as near transfer. In contrast, the transfer to non-executive domains like social cognition and emotional processing was identified as far transfer. The effect sizes of transfer domains in the included studies are detailed in Table 7. We classify transfer effects according to the magnitude of their effect sizes as follows: a transfer effect with an effect size below 0.2 is deemed small, an effect size ranging from 0.21 to 0.50 is characterized as medium, and an effect size surpassing 0.51 is designated as large. Table 3. Properties of behavioral assessments in the included studies Measurement (abbreviation; developer) Measure(s) Properties A 1 O 2 M 3 S 5 Barratt's Impulsiveness Scale (BIS-11; Patton et al., 1995) Impulsivity S O P S Alphabet Writing Task(AWT; Puranik et al., 2016) Writing S O P S Wechsler Individual Achievement Test (WIAT; Wechsler, 1992) Writing S O P S Gray Oral Reading Test (GORT; Wiederholt & Bryant, 1992) Reading S O P C S Abilità di calcolo aritmetico (ADCA; Lucangeli et al., 1998) Mathematics S O P S subitizing and counting (SC; Ashkenazi & Henik, 2012) Mathematics S O C ns Évaluation de la Lecture en FluencE (ELFE; Erika Godde, Marie-Line Bosse, 2021) Reading S O C ns Reading Test (RT; Cornoldi & Colpo, 1985) Reading S O P C SC Evaluación neuropsicológica infantil (ENI-2; Matute Esmeralda et al., 2014) Reading, writing, attention S O P C Piers Harris self-concept scale (PH; Huebner, 1994) self-perception S O P C Biomechanical tasks (BM; Olree & Vaughan, 1995) Motor activity S O M C Gates-MacGinitie Reading Test (GMG; W. MacGinitie, 1989) Reading S O P ns York Assessment of Reading for Comprehension (YARC; Martin, 2011) Reading S O P S Comprehensive Test of Phonological Processing (CTOPP-2; Tennant, 2014) Phonological awareness S O P S character-list reading task (CLRT; Zhao et al., 2017) Reading S O P ns sentence verification task (SVT; van den Boer et al., 2014) Reading S O C ns Sentence reading test (SRT; Zhao et al., 2017) Reading S O PC C single-character reading tasks (SCRT; Zhao et al., 2019) Reading S O P C Social Skills Rating System (SSRS; Gresham, F. M., & Elliot, 1990) Interpersonal skill T O P S State–Trait Anxiety Inventory (STAI; Gonzalez-reigosa & Io 1971) Psychological State S O P S Attitudinal questions( AQ; J. D. Beauchemin, 2008) Psychological State S O P S Non-Alphanumeric RAN Task ( NARANT; Mascheretti et al., 2018) Reading S O C ns Phoneme-Blending Task (PBT; Franceschini et al., 2013) Reading S O P C Phonology Basiskompetenzen für Lese-Rechtschreibleistungen (BAKO; Marie-Line Bosse , Marie Josèphe Tainturier, 2007) Phonological awareness S O C SH Rapid Automized Naming (RAN; Hugdahl, 1995) Reading S O C S Glasgow Anxiety Scale for individuals with Intellectual Disabilities (GAS-ID; Mindham & Espie, 2003) Psychological State S O P C Cognitive and Affective Mindfulness Scale—Revised (CAMS-R; Feldman et al. 2007) Psychological State S O P C Batteria per la Valutazione della Dislessia e DisortograWa Evolutiv (BVDDE; (G. Sartori, R. Job, 1995) Reading S O P C Text reading (TR; Cornoldi & Colpo, 1985) Reading S O P C Single word/non-word reading (SW/NWR; G. Sartori, R. Job, 1995) Words S O P C Spelling tests (ST; G. Sartori, R. Job, 1995) Writing S O P C Phonemic Awareness (PA; Cossu et al.) Phonological awareness S O P C Need Satisfaction (NS; Savard et al., 2013) Autonomy, competence, relatedness S O P S Behavior Assessment System for Children, Second Edition (BASC; Reynolds, 1997) Internalized Symptoms S O P S Mindfulness Measure (MM; Malboeuf-Hurtubise, Lacourse, Taylor, et al., 2017) Mindfulness S O P S Standardized Chinese Character Recognition Test( SCCRT; Liao et al., 2008) Reading S O P C Gates-MacGinitie Reading Comprehension Normal Curve Equivalence (GMNCE; MacGinitie et al., 43AD) Reading S O C ns Woodcock-Johnson Word Attack (WJWA; Mather & Jaffe., 2016) Reading S O C ns Affective Control Scale (ACS; Melka et al., 2011) Anger, depression, anxiety, positive affect S O P S Young Schema Questionnaire-Short Form (YSQ- SF; Young, 1998) Maladaptive schemas S O P S Abbreviations: 1. A: agent, C: clinician, P: parent, S: self-administered, T: teacher; 2. O: objectivity, O: objective, S: subjective, 3. M: material, C: computerized, P: paper and pencil; 4. S: setting, C: clinic, H: home, S: school, ADL: activity daily living, Symp: symptoms Table 4. Properties of cognitive assessments in the included studies Measurement (abbreviation, developer) Measure(s) 1 Properties A 2 O 3 M 4 S 5 Continuous Performance Test (CPT; Homack et al., 2014) Sustained attention S O C C Delis–Kaplan Executive Function System (DKEFS; Delis 2001) Attention, inhibition, switching, letter Fluency S O ns S Working Memory Tasks (WMT, Daneman, 1980) Working memory S O P S Attenzione e Concentrazione (AC; Di Nuovo, Santo, 2006) Attention S O C S Attention Networks Test and Interactions (ANT-I; Callejas et al., 2004) Attention S O C ns Visual attentional span (VAS; Marie-Line Bosse , Marie Josèphe Tainturier, 2007) Attention S O C ns Covert orienting of visual attention (COVA; Facoetti et al., 2003) Visual attention S O C C Test Of Variables of Attention (TOVA; Greenberg & Waldmant, 1993) Selective attention and inhibition. S O C C Test of Visual Perceptual Skills (TVPS; Martin, 2011) Visual skills S O C C Cognitive Assessment System (CAS; Naglieri, Jack A., 1997) Attention S O P ns Attention KITAP (AK; Marie-Line Bosse , Marie Josèphe Tainturier, 2007) Attention S O C S Attentional Blink Task (ABT; Lacroix et al., 2005) Attention S O C C Posner's task (PT; Posner et al., 1980) Attention S O C C Magnocellular temporal processing tasks (MTPT; Peters et al., 2021) Temporal processing S O C S Theory of visual attention based assessment (TVABA; Habekost, 2015) Working memory, attention S O C ns Visual 1-back task (VBT; Zhao et al., 2017) Visual Attention Span S O C C Visual Search Task (VST; Bertoni et al., 2021) Visual attention S O C ns Crowding task (CT; Yeshurun & Rashal, 2010) Visual attention S O C ns Focused and distributed spatial attention (FDSA; Marie-Line Bosse , Marie Josèphe Tainturier, 2007) Attention S O C C Cross-modal Attention Task (CAT; Petersen & Posner, 2012) Attention S O C C Auditory-Phonological Working Memory (APWM; Franceschini et al., 2017) Working memory S O P C Attention Shifting (AS; Franceschini et al., 2017) Visual, auditory, audio-visual processing and cross-sensory attentional shifting S O C C Digit Span (DS; Chenault et al., 2006) Working Memory S O C S Form-Resolving Field (FRF; Geiger et al., 1992; Lorusso et al., 2005) Visual perception S O C C Memory (M; Reynolds, 1997) Verbal memory, working memory, long term memory S O P C Coherent Motion Threshold (CMTI; Patel et al., 2011) Attention S O ns ns Note: 1. CF: cognitive flexibility, EF: executive function, Lang: language: IC: inhibitory control, RDM: risky decision making, STM: short-term memory, WM: working memory; 2. A: agent, S: self-administered; 3. O: objectivity, objective; 4. M: material, C: computerized, P: paper-pencil; 4. S: Setting, C: Clinic, H: Home, S: School Table 5. Description of interventions and properties Intervention (Abbreviation; Developer) Description (Respective included studies) Properties A 1 O 2 M 3 S 4 Mindfulness based cognitive therapy (MBCT; Gilbert & Procter, 2006) A program that combines mindfulness meditation with cognitive-behavioral techniques. It integrates mindfulness practices, cognitive therapy principles, and self-compassion strategies, encompassing training in body scan, breathing exercises, meditation, as well as fostering accepting and nonjudgmental focus and coping skills. C S Me SH Perceptual Accuracy-Visual Efficiency (PAVE; Groffman, S., & Press, 1989) This intervention encompasses tasks aimed at developing attention including detection, perceptual accuracy, visual search, visual span, visual scan, and guided reading tasks. S O PC ns Attentional Training (AT; Habekost, 2015) An attentional training regimen involves tasks such as the visual rapid discrimination task, visual short-term memory span task, and judgment of target orientation. S O C ns Fruit Ninja (https://fruitninja.com) Engaging in a task involving slicing fruits by moving the cursor on the screen while holding down the left button was part of the activity. In a different segment, eye movements were tracked using an infrared camera to control the cursor, with the goal of improving dynamic visual attention through precise and well-timed eye movements. S O C S Neuro VR (Pedroli et al., 2017) In the virtual reality environment, attentional tasks involve responding to a dynamically changing target among 3D objects on a blackboard, reacting to the letter "G" following the prime letter "A," and identifying a target color associated with a specific category among four colors on the blackboard while listening to a story. S O C C Visual Texture Discrimination Training (VTDT; Meng et al., 2014) In this visual perception experiment, participants fixated on a randomly rotated "T" or "L" presented at the bottom of a texture stimulus to indicate the fixation letter ("T" or "L") and then specify the target texture orientation (horizontal or vertical). Correct responses required accuracy in both the letter and target texture judgments, with no feedback given to participants. S O C C Flash Word Training (FWT; Masutto; Fabbro, 1995) A computerized program facilitates ocular fixation monitoring by tracking a luminous dot oscillating between the top and bottom of the screen at an adjustable speed. The word is displayed only when the child accurately clicks the mouse at the precise moment the dot crosses the central target. S O C C Dichotic Listening (DL; Bless et al., 2013) Participating in selective auditory attention training within a dichotic listening paradigm entailed utilizing constant vowel-syllables (/ba/, /da/, /ga/, /pa/, /ta/, /ka/), featuring six homonym pairs presented simultaneously to both ears. Displayed on a touch screen, subjects were directed to promptly select the correct syllable under forced right or left ear conditions, as well as in situations with no forced direction. C O C S CogniPlus (cogniplus co.) A computerized intervention incorporating tasks for alertness, visual-spatial attention, selective attention, and divided/focused attention training. S O C ns Celeco (Werth, 2007) A reading-specific attention training method involving the reorientation of attention during the systematic scanning of word fragments, aiming to promote smooth and targeted gaze movements. S O C ns Attention Training (AT; Di Nuovo, 2000) A comprehensive set of attention training tasks encompassing simple and choice reaction time, visual, visuo-spatial, and auditory selectivity, digit span, divided attention, resistance to distraction, and attentive shifting. S O C S MoveR (Gibert et al., 2023) A series of tasks within a virtual reality environment, including "Read in Motion" to enhance visual discrimination, attentional span, and promote focal visual attention; "Battlerace" to improve saccades and motor coordination; "Jump in Words" to enhance spatial orientation; and "Vergence Movements" to refine visual coordination. S O C C Beatgames (https://beatsaber.com) A set of gross motor coordination movements in a virtual reality setting where a specific musical rhythm by striking color cubes (red or blue) in a prescribed order and position to enhance visual attention, visual and motor coordination, and balance. S O C C GrafoTami (Oculus, 2015) An immersive virtual reality game involves object identification based on clue words across levels. Progressing through levels includes matching words, identifying corresponding phrases, and completing sentences, with complexity scaling based on syllables and sound structure. The interactive element incorporates a ball gun, where players read a red-colored clue word or sentence on a virtual wall, locate the corresponding word or object, and skillfully throw a ball at it. S O C C Visual Hemisphere-Specific Stimulation (VHSS; Bakker, Bouma, & Gardien, 1990) A series of ocular fixation tasks involves tracking a dot oscillating in the left or right visual field and responding when the dot reaches a central target. The complexity of the task heightens as strings of letters (words) become progressively more challenging in terms of length and frequency of use. S O C C Attention Training (AT; Thomson et al., 2005) A comprehensive attention training program comprises exercises focused on various aspects, including understanding and retaining auditory information and instructions, response speed, categorization of visual and auditory materials using verbal labels, visual search, motor response to visual and auditory targets, multitasking with evaluation of multiple categories, flexible task-switching, and sustaining focus on target stimuli amid auditory and visual distractions. S O P S Visual Attention Training (VAT; Caldani et al., 2020) A comprehensive attention training program comprises exercises focused on various aspects, including understanding and retaining auditory information and instructions, response speed, categorization of visual and auditory materials using verbal labels, visual search, motor response to visual and auditory targets, multitasking with evaluation of multiple categories, flexible task-switching, and sustaining focus on target stimuli amid auditory and visual distractions. S O C ns Visual Attention Span Training (VAST; Qian & Bi, 2015; Zhao et al., 2019) An array of attention training tasks, encompassing activities such as length estimation, digit cancelling, and visual search exercises. S O CP C Rayman Raving Rabbids (RRR; IGN co) An assortment of games, including shooting plungers at rhythm-dancing rabbits and engaging in various visual and auditory challenges. S O C C Call of Duty (infinityward) A series of challenges requiring rapid responses, mastery of weapon accuracy, exploration of new environments, and adept handling of multiple targets, all within the epic World War II battlefield as experienced through the perspectives of both civilians and soldiers. S O C H Abbreviations: 1. A: agent, C: clinician, P: parent, S: self-administered, T: teacher; 2. O: objectivity, O: objective, S: subjective, 3. M: material, C: computerized, Me: Mental, P: paper and pencil; 4. S: setting, C: clinic, H: home, S: school Table 6. Properties of assessments and intervention in the included studies Author, Year Intervention Assessment Name 1 (Agent 2 ) Setting 3 Dose Characteristic 4 :Name 5 Setting 2 Time 6 Alqarni & Hammad, 2021 MBCT (C) SH 600 SOP: BIS-11 S PP Ashkenazi & Henik, 2012 Call of Duty (S) H 1200 SOC: ANT-I & SC ns PP Azizi et al., 2018 APT(S) C 1000 SOC: CPT C PP Beauchemin et al., 2008 MBCT (C) SH 250 SOP : AQ & STAI, TOP: SSRS S PP Bertoni et al., 2019 RRR(S) ns 720 SOC : CT, SOP : RT, ns PP Bertoni et al., 2021 RRR (S) ns 1444 SOP: NARANT, RT, VST ns PP Caldani et al., 2020a AVAT (S) ns 10 SOC: ELFE, ET & VAS ns PP Chenault et al., 2006 AT (S) S 250 SOC : AWT, DKEFS, GORT, WIAT S PP Facoetti et al., 2003 VHSS (S) C 1440 SOP: COVA & RT C PP Flores-Gallegos et al., 2022 Beatgames (S), GrafoTami (S) C 900 SOC: TOVA, SOM: BMT, SOP: ENI-2, PHSCS C PP Franceschini et al., 2013 RRR (S) C 720 SOC : CAT & FDSA, SOP : PBT, RT C PPF Franceschini et al., 2017 RRR (S) C 720 SOC : AS & FDSA, SOP : APWM, RT C PP Gibert et al., 2023 MoveR (S) C 300 SOC : TVPS C PP Guarnera & D’Amico, 2014 AT (S) S 600 SOC : AC, SOP : ABCA, WMT S PP Pérez-Puelles et al., 2022 VideoGame (S) S 720 SOC : CPT, SA S PP Heim et al., 2015 CogniPlus (S) ns 600 SOC : AK, RT & PB, COD : fMRI SC PP Helland et al., 2018 DL (C) S ns SOC : DS, RAN S PP Jones & Finch, 2020 MBCT (C) CH 480 SOP : GAS-ID, CAMS-R C PP Lorusso et al., 2005 VHSS (S) C 1440 SOC : FRF, SOP : BVDDE C PP Lorusso et al., 2006 VHSS (S) C 1440 SOP : M, ST, SW/NWR, TPA, TR C PP Malboeuf-Hurtubise et al., 2017 MBCT (C) SH 480 SOP : MM & BASC-II, TOP : BASC-II S PP Malboeuf-Hurtubise et al., 2018 MBCT (C) SH 480 SOP : NS S PP Malboeuf-Hurtubise et al., 2019 MBCT (C) SH 400 SOP : BASC-II, NS S PPF Meng et al., 2014 VTDT (S) C 500 SOC : RT, SOP : SCCRT C PPF Pedroli et al., 2017 Neuro VR (S) C 320 SOC : ABT, PT, RT C PP Peters et al., 2021 Fruit Ninja (S) S 300 SOC : ET & MTPT, SOP : CTOPP-2, YARC S PP Ren, 2023 AT (S) ns 300 SOC : SVT & TVABA, SOP : CLRT ns PPF Solan et al., 2003 PAVE (S) ns 720 SOC : CAS, SOP : GMG c PP Solan et al., 2004 PAVE (S) ns 675 SOC : CMTI, GMNCE, GORT, WJWA ns PP Veysi et al., 2015 MBCT (C) SH 1440 SOP : ACS, YSQ-SF S PP Zhao et al., 2019 VAST (S) C 300 SOC : VBT, SOP : SCRT, SRT C PPF 1. AT: Attention Training, AVAT: Auditory-Visual Attention Training, DL: Dichotic Listening, FWT: Flash Word Training, MBCT: Mindfulness based cognitive therapy, PAVE: Perceptual Accuracy-Visual Efficiency, RRR: Rayman Raving Rabbids, VAST: Visual Attention Span Training, VAT: Visual Attentional Training, VHSS: Visual Hemisphere-Specific Stimulation, VTDT: Visual Texture Discrimination Training; 2. C: Clinician, P: Parent, S: Self, T: Teacher; 3. C: Clinic, H: Home, S: School; 4. Clinician, P: Parent, S: Self-administered, T: Teacher /O: Objective, S: Subjective / C: Computerized, P: Paper and pencil; 5. ABCA: Abilità di calcolo aritmetico, ABT: Attentional Blink Task, AC: Attenzione e Concentrazione, ACS: Affective Control Scale, AK: Attention KITAP, ANT-I: Attention Networks Test and Interactions, APWM: Auditory-phonological working memory, AS: Attention Shifting, AQ: Attitudinal Questions, AT: Attention Test, AWT: Alphabet Writing Task, BASC-II: Behavior Assessment Scale for Children, BIS-11: Barratt's Impulsiveness Scale, BMT: Biomechanical tasks, BVDDE: Batteria per la Valutazione della Dislessia e DisortograWa Evolutiv, CAS: Cognitive Assessment System, CAT: Cross-modal Attention Task, CLRT: character-list reading task, CMTI: Coherent Motion Threshold, COVA: Covert Orienting of Visual Attention, CPT: Continuous Performance Test, CT: Crowding Task, CTOPP-2: Comprehensive Test of Phonological Processing, DKEFS: Delis–Kaplan Executive Function System, DS: Digit Span, ELFE: Évaluation de la Lecture en FluencE, ENI-2: Evaluación neuropsicológica infantile, ET: Eye Tracking, FDSA: Focused and Distributed Spatial Attention, fMRI: Functional magnetic resonance imaging, FRF: FRF: Form-Resolving Field, CAMS-R: Cognitive and Affective Mindfulness Scale-Revised, GAS-ID: Glasgow Anxiety Scale for individuals with Intellectual Disabilities, GMG: Gates-MacGinitie Reading Test, GMNCE: Gates-MacGinitie Reading Comprehension Normal Curve Equivalence, GORT: Gray Oral Reading Test, M: Memory, MM: Mindfulness Measure, MTPT: Magnocellular temporal processing tasks, NARANT: Non-Alphanumeric Rapid Automized Naming Task, NS: Need Satisfaction, PA: Phonemic Awareness, PB: Phonology BAKO, PBT: Phoneme-Blending Task, PHSCS: Piers Harris self-concept scale, PT: Posner’s task, RAN: Rapid Automized Naming, RT: Reading Test, SA: Sustained Attention, SC: Subitizing and counting, SCCRT: Standardized Chinese Character Recognition Test, SCRT: single-character reading tasks, SRT: Sentence reading test, SSRS: Social Skills Rating System, ST: Spelling Test, STAI: The State–Trait Anxiety Inventory, SVT: sentence verification task, SW/NWR: Single Word/Non Word Reading, TOVA: Test of Variables of Attention, TR: Text Reading, TVABA: Theory of visual attention based assessment, TVPS: Test of visual perceptual skills, VAS: Visual Attention Span, VBT: Visual 1-back task, WIAT: Wechsler Individual Achievement Test, VST: Visual Search Task, WJWA: Woodcock-Johnson Word Attack, WMT: Working Memory Tasks, YARC: York Assessment of Reading for comprehsion, YSQ- SF: Young Schema Questionnaire-Short Form ; 6. PP: Pre-test and Post-test, PPF: Pre-test, Post-test and follow-up In detail, regarding functional transfer, 6 studies demonstrated no transfer (Alqarni & Hammad, 2021; Azizi et al., 2018; Heim et al., 2015; Pérez-Puelles et al., 2022; Helland et al., 2018; Malboeuf-Hurtubise et al., 2018), 4 studies exhibited small transfer effects (Ashkenazi & Henik, 2012; Bertoni et al., 2021; Pedroli et al., 2017; Zhao et al., 2019), 6 studies unveiled medium transfer effects (Chenault et al., 2006; Flores-Gallegos et al., 2022; Gibert et al., 2023; Guarnera & D’Amico, 2014; Ren, 2023; Solan et al., 2004), and 15 studies showcased large transfer effects (Beauchemin et al., 2008; Bertoni et al., 2019; Caldani, Gerard, et al., 2020; Facoetti et al., 2003; Franceschini et al., 2013; Franceschini et al., 2017; Jones & Finch, 2020; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2019; Meng et al., 2014; Peters et al., 2021; Solan et al., 2003; Veysi et al., 2015). For implemental transfer, 11 studies demonstrated no transfer effect(Alqarni & Hammad, 2021; Ashkenazi & Henik, 2012; Caldani, Gerard, et al., 2020; Chenault et al., 2006; Gibert et al., 2023; Pérez-Puelles et al., 2022; Heim et al., 2015; Malboeuf-Hurtubise et al., 2018; Pedroli et al., 2017; Solan et al., 2004; Zhao et al., 2019), 3 studies exhibited small transfer effects (Bertoni et al., 2021; Guarnera & D’Amico, 2014; Ren, 2023), 4 studies unveiled medium transfer effects (Flores-Gallegos et al., 2022; Franceschini et al., 2017; Helland et al., 2018; Malboeuf-Hurtubise et al., 2017), and 13 studies showcased large transfer effects (Azizi et al., 2018; Beauchemin et al., 2008; Bertoni et al., 2019; Facoetti et al., 2003; Franceschini et al., 2013; Jones & Finch, 2020; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2019; Meng et al., 2014; Peters et al., 2021; Solan et al., 2003; Veysi et al., 2015) . Ecological transfer reveals that 25 studies presented no transfer effect (Alqarni & Hammad, 2021; Azizi et al., 2018; Bertoni et al., 2019; Bertoni et al., 2021; Caldani, Gerard, et al., 2020; Chenault et al., 2006; Facoetti et al., 2003; Flores-Gallegos et al., 2022; Franceschini et al., 2013; Franceschini et al., 2017; Gibert et al., 2023; Guarnera & D’Amico, 2014; Pérez-Puelles et al., 2022; Heim et al., 2015; Helland et al., 2018; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2018; Meng et al., 2014; Pedroli et al., 2017; Peters et al., 2021; Ren, 2023; Solan et al., 2003; Solan et al., 2004; Zhao et al., 2019 ) . Furthermore, 2 studies uncovered moderate transfer effects (Ashkenazi & Henik, 2012; Malboeuf-Hurtubise et al., 2017) , and 4 studies illustrated substantial transfer effects (Beauchemin et al., 2008; Jones & Finch, 2020; Malboeuf-Hurtubise et al., 2019; Veysi et al., 2015). For the level domain, we observe that 10 studies failed to demonstrate any transfer effect (Alqarni & Hammad, 2021; Gibert et al., 2023; Guarnera & D’Amico, 2014; Pérez-Puelles et al., 2022; Heim et al., 2015; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2017; Solan et al., 2003; Veysi et al., 2015), whereas 5 studies exhibited minor transfer effects (Beauchemin et al., 2008; Franceschini et al., 2017; Malboeuf-Hurtubise et al., 2019; Pedroli et al., 2017; Solan et al., 2004). In addition, 3 studies revealed moderate transfer effects (Bertoni et al., 2021; Flores-Gallegos et al., 2022; Ren, 2023), and 13 studies showcased significant transfer effects (Ashkenazi & Henik, 2012; Azizi et al., 2018; Bertoni et al., 2019; Caldani, Gerard, et al., 2020; Chenault et al., 2006; Facoetti et al., 2003; Franceschini et al., 2013; Helland et al., 2018; Jones & Finch, 2020; Malboeuf-Hurtubise et al., 2018; Meng et al., 2014; Peters et al., 2021; Zhao et al., 2019 ). For the durability, it's evident that 28 studies did not show any transfer effects (Alqarni & Hammad, 2021; Ashkenazi & Henik, 2012; Azizi et al., 2018; Beauchemin et al., 2008; Bertoni et al., 2019; Bertoni et al., 2021; Caldani, Gerard, et al., 2020; Chenault et al., 2006; Facoetti et al., 2003; Flores-Gallegos et al., 2022; Franceschini et al., 2013; Franceschini et al., 2017; Gibert et al., 2023; Guarnera & D’Amico, 2014; Pérez-Puelles et al., 2022; Heim et al., 2015; Helland et al., 2018; Jones & Finch, 2020; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2017; Malboeuf-Hurtubise et al., 2018; Pedroli et al., 2017; Peters et al., 2021; Solan et al., 2003; Solan et al., 2004; Veysi et al., 2015; Zhao et al., 2019). In addition, 1 studies revealed moderate transfer effects (Ren, 2023), and 2 studies showcased significant transfer effects (Malboeuf-Hurtubise et al., 2019; Meng et al., 2014). In sum, for all FIELD’s domains, we find that 2 studies did not yield any transfer effects (Alqarni & Hammad, 2021; Pérez-Puelles et al., 2022), while 10 studies exhibited minor transfer effects (Ashkenazi & Henik, 2012; Azizi et al., 2018; Bertoni et al., 2021; Chenault et al., 2006; Guarnera & D’Amico, 2014; Heim et al., 2015; Helland et al., 2018; Pedroli et al., 2017; Solan et al., 2004; Zhao et al., 2019). Moreover, 9 studies unveiled moderate transfer effects (Caldani, Gerard, et al., 2020; Flores-Gallegos et al., 2022; Franceschini et al., 2013; Franceschini et al., 2017; Gibert et al., 2023; Jones & Finch, 2020; Malboeuf-Hurtubise et al., 2017; Malboeuf-Hurtubise et al., 2019; Ren, 2023), and 10 studies demonstrated significant transfer effects (Beauchemin et al., 2008; Bertoni et al., 2019; Facoetti et al., 2003; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2018; Meng et al., 2014; Peters et al., 2021; Solan et al., 2003; Veysi et al., 2015). Table 7. The effect sizes of included studies in the FIELD’s domains Author (Year) Cohen’s D (95% Confidence Interval) F I E L D S Alqarni & Hammad, 2021 - - - - - - Ashkenazi & Henik, 2012 .12 (0, .24) - .33 (.11, .55) 1.3 (1.2, 1.41) - .11 (-.02, .25) Azizi et al., 2018 - .71 (.37, 1.04) - .89 (.23, 1.55) - .14 (-.01,.29) Beauchemin et al., 2008 1.3 (1.2, 1.41) 1.3 (1.2, 1.41) 1.3 (1.2, 1.41) .04 (-.02, .1) - 1.04 (.84, 1.24) Bertoni et al., 2019 .89 (.23, 1.55) .89 (.23, 1.55) - .85 (.48, 1.21) - .53 (.08, .98) Bertoni et al., 2021 .04 (-.02,.1) .04 (-.02, .1) - .28 (.16, .39) - .02 (-.02, .07) Caldani et al., 2020a .85 (.48, 1.21) - - 4.69 (3.23, 6.16) - .34 (.09, .58) Chenault et al., 2006 .28 (.16, .39) - - .75 (.63, .87) - .11 (.02, .2) Facoetti et al., 2003 4.69 (3.23, 6.16) 4.69 (3.23, 6.16) - .59 (.33, .86) - 2.82 (1.53, 4.1) Flores-Gallegos et al., 2022 .42 (.18, .67) .42 (.18, .67) - .32 (.19, .46) - .32 (.09, .55) Franceschini et al., 2013 .59 (.33, .86) .59 (.33, .86) - 1.11 (.98, 1.23) - .35 (.12, .59) Franceschini et al., 2017 .6 (.33,.87) .32 (.19, .46) - .14 (-.04, .32) - .25 (.08, .42) Gibert et al., 2023 .28 (.1, .45) - - - - .28 (.11, .44) Guarnera & D’Amico, 2014 .25 (.05, .45) .14 (-.04, .32) - - - .11 (-.05, .26) Pérez-Puelles et al., 2022 - - - - - - Heim et al., 2015 - - - - - .19 (.06, .31) Helland et al., 2018 - .3 (.24, .36) - 1.33 (.51, 2.16) - .06 (.02, .1) Jones & Finch, 2020 .51 (.5,.51) .51 (.5, .51) .51 (.5, .51) 1.69 (1.31, 2.08) - .3 (.13, .47) Lorusso et al., 2005 1.62 (.96, 2.29) 1.33 (.51, 2.16) - - - .86 (.17, 1.54) Lorusso et al., 2006 1.69 (1.31, 2.08) 1.69 (1.31, 2.08) - - - 1.02 (.63, 1.41) Malboeuf-Hurtubise et al., 2017 .44 (.32,.56) .44 (.32, .56) .44 (.32, .56) - - .26 (.12, .41) Malboeuf-Hurtubise et al., 2018 - - - 1.81 (.97, 2.64) - 1.2 (.63, 1.78) Malboeuf-Hurtubise et al., 2019 .6 (.49,.71) .6 (.49, .71) .6 (.49, .71) .12 (.02, .22) .6 (.49, .71) .48 (.35, .61) Meng et al., 2014 1.81 (.97, 2.64) 1.81 (.97, 2.64) - 2.88 (1.94, 3.83) 2.56 (2.07, 3.05) 1.6 (1.01, 2.18) Pedroli et al., 2017 .12 (.02,.22) - - .15 (.08, .23) - .05 (-.02, .12) Peters et al., 2021 4.13 (3.27, 5) 2.88 (1.94, 3.83) - .72 (.35, 1.08) - 1.98 (1.19, 2.77) Ren, 2023 .38 (.3,.46) .15 (.08, .23) - .42 (.09, .74) .38 (.3, .46) .21 (.13, .29) Solan et al., 2003 .72 (.35, 1.08) 1.74 (1.59, 1.89) - - - .63 (.33, .93) Solan et al., 2004 .42 (.09,.74) - - .1 (.08, .12) - .17 (-.05, .38) Veysi et al., 2015 3.15 (2.96, 3.34) 3.15 (2.96, 3.34) 3.15 (2.96, 3.34) - - 1.89 (1.39, 2.39) Zhao et al., 2019 .1 (.08, .12) - - 1.3 (1.2, 1.41) - .04 (.02, .06) Overall .78 (.63, .93) .88 (.7, 1.05) 1.05 (.52, 1.58) .7 (.52, .88) 1.05 (.56, 1.55) .37 (.29, .45) DL (I 2 , p) 99.1%, <.001 99.9%, <.001 99.5%, <.001 98%, <.001 97.5%, <.001 93.1%, <.001 Abbreviations. F: function, I: implements, E: ecology, L: level, D: durability, S: sum of all domains Table 8. Subgroup classification of transfer effect size P value Heterogeneity Number of Studies Hedges' g (CI %95) Groups Potential Factors <.001 89.8% 25 .28 (.21, .35) <12 Participants’ Age (Yr.) <.001 97.8% 3 .99 (.12, 1.85) ≥12 <.001 94.4% 17 .39 (.27, .51) < 12 Intervention Dose (Hour) <.001 91.6% 12 .52 (.32, .72) ≥ 12 <.001 94.8% 7 1.01 (.63, 1.4) AST Intervention Level <.001 87% 23 .22 (.16, .29) APT <.001 94.8% 7 1.01 (.63, 1.4) Combined setting Intervention Setting . 0 1 .11 (-.02, .25) Home <.001 90% 11 .35 (.21, .49) School <.001 87.1% 4 .16 (0, .33) Clinic <.001 94.8% 7 1.01 (.63, 1.4) Mental Intervention Material <.001 87.6% 17 .29 (.19, .39) Computerized . 0 1 .11 (.02, .2) Paper & pencil .007 80.1% 3 .2 (0, .39) Virtual Reality <.001 93.3% 2 .32 (-.26, .9) Both .053 .932 .529 37.1% 0% 0% 19 2 8 .09 (.04, .14) .11 (.01, .21) .24 (.12, .36) Dyslexia Dyscalculia SLD SLD Subtype <.001 93.1% 31 .37 (.29, .45) All studies Abbreviations. F: function, I: implements, E: ecology, L: level, D: durability, S: sum of all domains, N: number of studies Table 8 presents a detailed subgroup classification of transfer effects based on various potential factors. As illustrated in the table, adolescents and adults demonstrated a moderate transfer effect, whereas children exhibited a substantial transfer effect. In terms of intervention dosage, it is noteworthy that a longer duration of intervention yields a more favorable impact on transfer. Specifically, the transfer effect for shorter interventions (12 hours and below) is characterized as moderate, while interventions of a more extended duration (12 hours and beyond) result in a large transfer effect. Considering the intervention level, it is observed that AST displayed a significant transfer effect, whereas APT had a relatively minor impact. Examining intervention settings, combined settings emerged as the most effective, showcasing a large transfer effect compared to other settings. Additionally, school-based interventions revealed a moderate transfer effect, while home and clinic settings exhibited a smaller transfer effect. Turning to intervention materials, mental material demonstrated a substantial transfer effect. Computerized and combined interventions showed a moderate transfer effect, whereas paper-based and virtual reality interventions depicted a smaller transfer effect. Addressing the SLD subtype, it is noteworthy that the transfer effect in both subtypes was relatively similar and relatively small. However, the combined subtype revealed a medium effect size for transfer. Discussion The aim of this study was to undertake a thorough examination and analysis of the transferability of attention training in individuals with SLD. To assess transferability, we utilized the FIELD framework, encompassing function, implement, ecology, level, and duration. Our results highlighted the substantial impact of participant age, intervention method, level, and setting on the outcomes of transferability. The relevance of age. The age factor significantly influences the effectiveness of interventions and investigations. Early intervention enhances outcomes for the majority of children with learning, attention, and cognition disorders (Pratt & Patel, 2007). When examining transferability, it consistently emerged that younger participants experienced a more prominent transfer effect compared to their older counterparts. A previous study on inhibitory control training in healthy individuals hints at the prospect of transferability to various cognitive functions in children, but this effect does not seem to extend to adults (Zhao et al., 2018). In another study involving inhibitory training with healthy adults and adolescents, positive outcomes were observed in both groups, indicating an enhanced inhibitory control. Nevertheless, the transfer pattern varied across age groups, with a more formative effect noted in the adolescents (WANG et al., 2020). The heightened potential for transferability in children and adolescents can be attributed to the greater plasticity of their developing brains (Hensch & Bilimoria, 2012; Park & Mackey, 2022). As children and adolescent brains undergo prolonged structural and functional reorganization, making them more responsive to training-induced changes (Dumontheil, 2016). However, it is important to note that the number of studies involving adults in our review was limited (only 3 out of 31), which restricts the strength and generalizability of any conclusions drawn about age-related differences. This sample imbalance should be taken into account when interpreting findings and highlights the need for future studies focusing on adult populations with SLD. The relevance of intervention dose. Concerning intervention dosage, it is important to highlight that a prolonged intervention duration has a more positive influence on transfer outcomes. To elaborate, shorter interventions (12 hours and below) exhibit a moderate transfer effect, whereas interventions lasting beyond 12 hours manifest a substantial transfer effect. Intensive intervention has been identified as a pivotal factor influencing the response to treatment in individuals with SLD (Reschly, 2014). A preceding study uncovered that the variance in the dosage and frequency of interactive book reading does not seem to impact word learning among children with developmental language disorders (Storkel et al., 2019). It is noteworthy to emphasize that attentional intervention represents a fundamental approach within the cognitive foundations of reading or mathematics, rather than merely repeating impaired skills in reading or calculation. A comparison between the outcomes of the current study and those of the earlier one underscores the significance of cognitive training over raw repetition for improvement. Importantly, the enhancement observed after skill training can be attributed to the compensation of impaired underpinnings with stronger components (Nejati, 2022). Considering the dosage aspect, it is essential to take into account the distribution of the intervention over time as another influencing factor. Previous studies have shown that distributed practice, as opposed to massed practice, results in enhanced learning outcomes. This holds true for both typically developing children (Haq & Kodak, 2015) and children with specific language impairment (Desmottes et al., 2017). In the present review, interventions were more evenly distributed in high-dose interventions compared to low-dose interventions, with 11.53 sessions over 6 weeks as opposed to 18.14 sessions over 8.2 weeks. The relevance of intervention level. The findings indicate a substantial transfer effect for AST, contrasting with the relatively minor impact observed for APT. It is noteworthy that despite their methodological differences, APT employing objective cognitive tasks and AST utilizing subjective behavioral tasks, both interventions share a common focus on attention as a fundamental cognitive function. It is noteworthy to consider that the duration of AST was longer than APT, with 10 hours for the former and 40 hours for the latter. This discrepancy in duration should be taken into account when interpreting the results. While our current study did not incorporate research specifically focused on practicing academic skills for SLD, earlier studies have identified the benefits of breaking down these skills into their components for intervention. For example, a review study categorized occupational therapy interventions for SLD into "occupation-as-means" and "occupation-as-outcome" (Bray et al., 2021). The former employs occupation as the intervention in therapy, focusing on the 'means,' while the latter views the intervention as the outcome or 'end' of therapy (AOTA, 2021). This study discovered that interventions promoting self-management and utilizing occupation-as-means were particularly effective. Furthermore, one possible explanation for the greater transfer effect observed in AST is its emphasis on metacognitive and self-regulatory mechanisms. AST interventions often engage participants in mindful attention, emotion regulation, and context-sensitive behavior (Quaglia et al., 2019; Tang & Posner, 2009; Wadlinger & Isaacowitz, 2011), which may enhance the generalizability of learned skills to real-world settings. In contrast, APT tends to isolate cognitive components in a decontextualized manner (Nejati, 2021; Nejati & Derakhshan, 2024), which may limit the scope of transfer. The relevance of intervention setting. Moreover, interventions conducted within the school setting demonstrated a moderate transfer effect, whereas those implemented in home and clinic settings exhibited a comparatively smaller transfer effect. Previous research involving children with learning disabilities found that collaboration between therapists, parents, and the school not only strengthens but also enhances the effectiveness of interventions (Reschly, 2014). The relevance of intervention material. The results suggest that mental materials exhibited a significant transfer effect. Computerized and combined interventions demonstrated a moderate transfer effect, while paper-based and virtual reality interventions revealed a small transfer effect. A previous meta-analysis indicated that computer-assisted interventions for mathematical training in students with learning disabilities did not demonstrate conclusive effectiveness, despite relatively large effect sizes (Seo & Bryant, 2009). A preceding study, comparing various intervention materials, indicates a widespread impact of cognitive strategy and direct instruction models in addressing academic difficulties in students with learning disabilities. However, the findings propose that the most impactful instructional approach is a combined model, as it yields the largest effect size (Swanson, 1999). The relevance of intervention SLD Subtype. The findings indicate that the transfer effect in both subtypes was notably comparable and relatively modest. Nevertheless, the combined subtype exhibited a moderate effect size for transfer. In a previous meta-analysis, it was uncovered that students with comorbid learning disorders exhibit distinct response patterns compared to those with specific mathematical disabilities. This discrepancy in response is contingent upon the nature of the mathematics intervention, suggesting the potential existence of a unique subtype. Conversely, students grappling with reading problems appear to respond uniformly to interventions, irrespective of whether their reading issues occur independently or in conjunction with mathematical disabilities (Fuchs et al., 2013). Limitations and future directions. Several limitations should be acknowledged for this study. Firstly, the generalizability of the findings may be constrained by the specific characteristics of the participant pool and the selected interventions. The study primarily focused on attention training, and as such, the transferability insights may not be universally applicable to interventions targeting different cognitive domains. Notably, 17 out of the 31 included studies were non-randomized (Table 2), which may limit the strength of causal inferences drawn from the findings. In addition, the sample was predominantly composed of children and adolescents, with only 9 studies including adult participants, thereby restricting the generalizability of results to adult populations with SLD. Furthermore, several studies, particularly those involving mindfulness-based interventions, used pre–post designs without control groups, raising concerns about potential placebo effects and limiting the ability to draw firm causal conclusions. Although a risk-of-bias assessment table was included to enhance transparency, future meta-analyses would benefit from stricter inclusion criteria prioritizing randomized controlled trials. Furthermore, the limited number of studies focusing on attentional state training (7 out of 31) restricts the strength of our conclusions regarding this intervention and limits the validity of direct comparisons with other training types. It is important to note that studies specifically targeting dyscalculia and dysgraphia were limited in number, which restricts the generalizability of our conclusions across all SLD subtypes. Future research should expand this line of inquiry to include other domains such as dys-orthographia and systematically evaluate the role of attention in diverse academic difficulties. These nuanced insights highlight the importance of tailoring interventions to specific age groups, utilizing diverse materials, and considering the unique characteristics of different SLD subtypes. Integrating these multifaceted considerations into future interventions for SLD holds the potential to significantly enhance cognitive training and skill improvement. Conclusion Consistently, the study revealed a more pronounced transfer effect in younger participants, those with combined SLD, interventions with longer durations, and those utilizing mental materials compared to paper-based and computerized interventions. Furthermore, AST demonstrated superior transfer effects compared to APT. The collaborative efforts of researchers, educators, and therapists are deemed essential in developing targeted interventions that effectively address the diverse needs of individuals with learning disabilities. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7853101","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":593331713,"identity":"849e5301-a95e-4ca8-8f0a-22b56823e47b","order_by":0,"name":"Vahid Nejati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACgwMMCUDShsEAwpcgrMUCpOWAQRoJWmwOAIkDDIdhWogANscbHn7+UHDe3lwigfHDDwaLfIJazM4cSJY4YHA7ceeMBGbJHgYJywaCWm4kJIC0JBjcSGCQBvqFsAON7z9I/nHA4Jw9UAvzb6K0GN5gSAPacoBxw40ENuJsMTyTkGZxxiA5cWfPwzbLHgMitBgcP5N8o+KPnb05e/LhGz8q6ogJbJ4EKIOxgYHI2GE/QJSyUTAKRsEoGMEAADZqPm+SL25SAAAAAElFTkSuQmCC","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":true,"prefix":"","firstName":"Vahid","middleName":"","lastName":"Nejati","suffix":""},{"id":593331714,"identity":"b7c72e44-9cd4-4888-8246-c79e03c6f7d7","order_by":1,"name":"Fatemeh Ghafouri","email":"","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Ghafouri","suffix":""},{"id":593331715,"identity":"a798c70e-f50f-49d9-9371-17853c1b43c3","order_by":2,"name":"Maedeh Hejazi","email":"","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":false,"prefix":"","firstName":"Maedeh","middleName":"","lastName":"Hejazi","suffix":""},{"id":593331716,"identity":"7ae3d6b2-24b9-41b2-abc8-1a29defea76c","order_by":3,"name":"Roozbeh Behroozmand","email":"","orcid":"","institution":"The University of Texas at Dallas","correspondingAuthor":false,"prefix":"","firstName":"Roozbeh","middleName":"","lastName":"Behroozmand","suffix":""}],"badges":[],"createdAt":"2025-10-14 02:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7853101/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7853101/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103178702,"identity":"3fe29b9a-ca49-4757-a3c0-2d5c4371ccca","added_by":"auto","created_at":"2026-02-22 17:06:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68525,"visible":true,"origin":"","legend":"\u003cp\u003eData extraction diagram for review. 31 studies were entered into our study out of 3944 initial candidate studies. 163 studies in the eligibility phase.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7853101/v1/7d03f7846aa4a40e6294c5e7.png"},{"id":103505809,"identity":"51655772-d560-4190-860d-494537b76ef6","added_by":"auto","created_at":"2026-02-26 13:33:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2106070,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7853101/v1/43b9a8b5-a2ba-4fe2-86df-31c08979929e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effectiveness of Attention Training in Specific Learning Disorders:A Systematic Review and Transfer Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpecific Learning Disabilities (SLD) are characterized by persistent and significant difficulties in acquiring and effectively utilizing skills related to reading (dyslexia), writing (dysgraphia), or mathematics (dyscalculia). These challenges persist despite individuals receiving conventional instruction, possessing intact sensory and motor functioning, normal intelligence, proper motivation, and sufficient sociocultural opportunities (APA, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the definition of SLD, the impairment is attributed to central information processing, cognitive functions, rather than the initial incoming sensory information or the later outgoing motor functions. A variety of cognitive impairment have been described in individuals with SLD including impaired perception (Yazdani et al., \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), attention, inhibitory control (Szucs et al., \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and working memory (Maehler \u0026amp; Schuchardt, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Schuchardt et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and cognitive flexibility (Cartwright et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Attention plays a pivotal role in cognitive functions, serving as a crucial element for selecting relevant information for perception, inhibiting irrelevant information, maintaining information in working memory for subsequent processing, and facilitating the seamless shift between different pieces of information to enhance cognitive flexibility (Nejati, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the neural level, dyslexia, dysgraphia, and dyscalculia are each linked to specific neural substrates. Dyslexia is commonly associated with dysfunction in the left temporoparietal regions and the visual word form area, which are critical for reading processes. Dysgraphia has been connected to altered activity in premotor and parietal areas involved in writing and motor planning. Dyscalculia is related to atypical functioning of the intraparietal sulcus, a region essential for numerical processing and quantity representation (Saini et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These structures could be considered as neural correlates of attention. The visual word form area is part of both language and attention circuitry (Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It shows strong intrinsic connectivity with fronto-parietal networks implicated in attentional control. Furthermore, its connection with the dorsal fronto-parietal attention network has been found to predict visuospatial attention (Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, the premotor cortex, receiving input from the posterior parietal cortex, is involved in spatial orientation and primarily regulates proximal motor functions (Abe \u0026amp; Hanakawa, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The intraparietal sulcus is consistently activated in tasks requiring selective attention, particularly when individuals must filter target stimuli from competing distractors (Brown et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, when it comes to symptoms of SLD, difficulties in reading, writing, or calculation may often be linked to underlying attentional impairments. Attention plays a foundational role in the functions that are essential for academic skill acquisition across domains. In reading, attention supports multiple stages, including word recognition, fluency, and comprehension. Sustained attention facilitates the acquisition of grapheme-phoneme correspondences and the decoding of unfamiliar words (Macdonald et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Attentional control is also critical for effective phonological processing, enabling learners to shift between phonological representations and suppress irrelevant stimuli (Kibby et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For reading comprehension, attentional mechanisms, along with working memory, allow readers to maintain textual information, integrate meaning across sentences, and selectively focus on relevant content (Dehn, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn writing, attention is implicated in a range of components, including handwriting, spelling, vocabulary use, and overall written expression (Reid et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Prior research, particularly in individuals with ADHD, has shown associations between attention deficits and poor writing performance, shorter text with more errors (Graham et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Re et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Moreover, writing is supported by a broader network of cognitive processes\u0026mdash;including short-term memory, working memory, and executive functions, that are sensitive to attentional disruption (Cheng et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, mathematical learning relies heavily on attentional control. Solving multi-step problems requires the ability to sustain focus, inhibit distractions, and manage sequential operations (Commodari \u0026amp; Di Blasi, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Teacher-rated inattention has been shown to explain unique variance in children's math performance, independent of other cognitive factors (Cirino et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Fuchs et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Notably, attention has emerged as a stronger predictor of arithmetic skills and problem-solving accuracy than working memory (Fuchs et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Sustained attention, in particular, is closely linked to arithmetic achievement (Orbach \u0026amp; Fritz, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), suggesting that attentional impairments may hinder the acquisition and execution of mathematical procedures (Calub et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Altogether, these findings suggest that attentional functioning is not merely a co-occurring difficulty in SLD, but may play a mechanistic role in the emergence and persistence of academic deficits. Targeting attention in intervention design may therefore serve as a foundational strategy for enhancing learning outcomes across domains.\u003c/p\u003e \u003cp\u003eThe central role of cognitive function in SLD makes cognitive training a potential intervention candidate. Cognitive training is defined as \"the process of remediation or compensation for cognitive deficits and related outcomes through well-established programs by the therapist\" (Nejati, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While cognitive training can address various cognitive impairments, attention, as a fundamental cognitive function, holds particular significance for training. Two prevalent attention training interventions include Attentional State Training (AST) and Attentional Process Training (APT). AST focuses on teaching individuals to regulate their attentional state and arousal levels through techniques like breathing exercises, meditation, and relaxation methods. On the other hand, APT concentrates on training specific attentional processes by guiding individuals through progressive tasks that involve selective, sustained, shifting, and divided attention (Posner et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tang \u0026amp; Posner, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies used AST ( Alqarni \u0026amp; Hammad, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Beauchemin et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Jones \u0026amp; Finch, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Veysi et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bertoni et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Malboeuf-Hurtubise et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Malboeuf-Hurtubise et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Malboeuf-Hurtubise et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e ) and APT (Ashkenazi \u0026amp; Henik, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Azizi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bertoni et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bertoni et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Caldani et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chenault et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Facoetti et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Flores-Gallegos et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Flores-Gallegos et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Franceschini et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Franceschini et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gibert et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Guarnera \u0026amp; D\u0026rsquo;Amico, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; P\u0026eacute;rez-Puelles et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Heim et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Helland et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lorusso et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lorusso et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pedroli et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Peters et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ren, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) for training of individuals with SLD.\u003c/p\u003e \u003cp\u003eWhile acknowledging the significance of attention as a fundamental cognitive process in the management of individuals with SLD, the primary challenge within this group pertains to academic performance at the behavioral level. Certainly, when cognitive training shows positive efficacy in enhancing the targeted cognitive function but fails to translate into improved academic performance in SLD, its overall effectiveness comes into question. Certainly, the focus of specific interventions should extend beyond test results within the same specific domain and be directed towards addressing behavioral symptoms. The transmission of training effects from a trained domain to untrained domains referred to as transfer (Haskell, 2001). Transferability serves as a crucial metric for evaluation of intervention effectiveness, determining whether an intervention targeting the core pathology can bring about improvements that span all affected domains. In the context of the FIELD model, transfer encompasses five essential dimensions: Function, Implement, Ecology, Level, and Durability (Nejati, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \"Function\" pertains to the ability to transfer performance from a trained function, such as imitation, to an untrained function, such as theory of mind. The \"Implement\" dimension involves various materials and methods used in the assessment and training process, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eDemonstrating improvement through diverse assessment tools, rather than intervention materials and methods, signifies implement transfer. \"Ecological transfer\" entails transferring intervention effects from a specific setting, such as a clinical environment, to an entirely different and new setting, like the home. \"Level transfer\" focuses on transferring training effects across different levels, including neural, cognitive, and behavioral levels. Lastly, \"Durability\" examines the duration of the training effects and how long they persist after discharge.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of different transfer domains based on FIELD model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe transfer of training effect from trained function to an untrained function(s).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImplement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImprovement demonstrated through diverse assessment tools during the assessment and training process.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe transference of intervention effects from one specific setting (e.g., clinic) to an entirely different and new setting (e.g. home).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe transfer of training effects across different levels, including neural, cognitive, and behavioral levels.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcerned with how long the effects of training persist after discharge.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eDespite its critical importance, the issue of transfer has remained a topic of ongoing debate in the cognitive training literature\u0026nbsp;(Greenwood \u0026amp; Parasuraman, 2016; Smid et al., 2020; Westwood et al., 2023). While numerous studies have reported improvements in trained cognitive tasks, evidence for transfer, particularly transfer to academic performance and everyday functioning, remains inconsistent. This inconsistency raises important concerns regarding the ecological validity and long-term utility of cognitive training interventions (Noack et al., 2014). In many cases, training gains are limited to task-specific improvements, with minimal evidence of meaningful changes in real-life skills or academic achievement (Westwood et al., 2023).\u003c/p\u003e\n\u003cp\u003eOne major challenge in this field is the absence of a unified definition and standardized criteria for evaluating transfer. Some studies operationalize transfer narrowly\u0026mdash;such as improvements in tasks that are structurally similar but not identical to the trained tasks\u0026mdash;while others use broader behavioral or academic outcomes, leading to considerable heterogeneity in findings. Additionally, methodological variability across studies, such as differences in training duration, outcome measures, population characteristics, and the presence or absence of control conditions, further complicates the interpretation of results.\u003c/p\u003e\n\u003cp\u003eBy adopting the FIELD model, the current study seeks to contribute to this ongoing discourse by offering a more structured and multidimensional approach to conceptualizing transfer. Rather than relying solely on task-based outcomes or surface-level performance shifts, the FIELD model allows for a comprehensive evaluation of how training effects generalize across functions, materials, contexts, hierarchical levels, and over time. This framework not only provides a conceptual scaffold for organizing existing evidence but also guides future research in systematically assessing the real-world relevance and sustainability of cognitive training effects.\u003c/p\u003e\n\u003cp\u003eIn the current study, our objective was to investigate the transferability of attention training in individuals with SLD across diverse domains, using the FIELD model. Additionally, we aimed to identify significant determinants influencing the transferability of attention training.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e The methodology for this systematic review is outlined below and adheres to the guidelines set forth by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The review protocol has been registered on the PROSPERO website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.crd.york.ac.uk/PROSPERO\u003c/span\u003e\u003cspan address=\"http://www.crd.york.ac.uk/PROSPERO\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) under registration number CRD42023462407.\u003c/p\u003e \u003cp\u003e\u003cb\u003eStudy Selection Criteria.\u003c/b\u003e Type of Studies. This systematic review encompasses diverse study designs. The focus is on interventions involving attention training. Exclusion criteria encompass reviews, newspapers, book chapters, notes, surveys, letters to the editor, case reports, and case series. No restrictions are imposed on language or geographic location. All studies involving individuals diagnosed with specific learning disorders are considered. There are no limitations regarding age, gender, or nationality. Specific learning disorders may include reading impairment (dyslexia), impairment in written expression, and impairment in mathematics (dyscalculia), Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e\u003cb\u003eSearch Strategy.\u003c/b\u003e A comprehensive search was conducted across multiple databases, including PubMed, Scopus, Web of Science, Eric, APA PsycINFO (via EBSCO), and Cochrane Central Register of Controlled Trials (CENTRAL). The search strategy incorporated terms related to attentional training and learning disability, with adaptations made for each specific database. For instance, the search syntax used for PubMed was as follows: (\"attention rehabilitation\"[tiab] OR \"attention training\"[tiab] OR \"attentional enhancement\"[tiab] OR \"attentional intervention\"[tiab] OR mindfulness[tiab] OR yoga[tiab]) AND (Dyscalculia[tiab] OR \"mathematics disorder\"[tiab] OR Dyslexia[tiab] OR \"reading disorder\"[tiab] OR \"reading disability\"[tiab] OR Dysgraphia[tiab] OR \"writing disorder\"[tiab] OR \"specific learning disorder\"[tiab] OR \"learning disability\"[tiab]). The study selection process is visually represented in a flowchart based on PRISMA guidelines. The full search strategy for databases is provided in the supplementary material. Additionally, bibliographies of relevant prior reviews and primary studies identified by the search strategy were scrutinized for additional pertinent papers. No restrictions were applied based on language or geography. Searches were conducted from the beginning of 1990 to the end of June 2023.\u003c/p\u003e \u003cp\u003e\u003cb\u003eData Extraction.\u003c/b\u003e Search results were imported into Mendeley software to identify and eliminate duplicate records. Subsequently, one researcher (MH) screened titles and abstracts based on predetermined eligibility criteria. Following this, two independent researchers (MH and FG) conducted a full-text screening of eligible studies, resolving discrepancies through discussion or consultation with a third researcher (VN), if needed. Data extraction from primary articles was independently performed by two reviewers (FG, MH) using a predefined form. Any discrepancies were resolved through consensus between the two reviewers, with a third reviewer (VN) acting as an arbitrator when necessary. Extracted data included the first author's name, publication year, study country, design, intervention type, sample size, participant characteristics (gender, age, type of SLD), blinding, and various outcomes (behavioral, cognitive, neural).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk of Bias.\u003c/b\u003e Risk of bias assessments were conducted using the Cochrane extension tool, categorizing studies into low, high, or unclear risk of bias. Two investigators (FGH, MH) independently rated each study, resolving differences through consensus or consulting a third investigator (VN).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"609\" height=\"447\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransfer Measurement and Analysis.\u003c/strong\u003e In accordance with the FIELD model of transfer, each assessment test is assigned a value within the FIELD framework. The effect size of the intervention FIELD is subsequently juxtaposed with the effect size of the assessment FIELD, serving as the gauge for transfer. To illustrate, let\u0026apos;s consider the Continuous Performance Test (CPT), measuring sustained attention (F). Administered through a computer as an objective test (I), conducted in a clinical setting (E), and serving as a cognitive measure (L), the CPT is administered across three sessions\u0026mdash;pre-intervention, post-intervention, and follow-up (D). Additionally, we consider an objective computerized attention training in a clinical setting. Any disparities observed between the FIELD domains signify a transfer effect. Consequently, the effect size of the respective test is deemed the measure of transfer in this study. The transfer measure within each FIELD\u0026rsquo;s domain is computed by summing up the effect sizes of the corresponding assessments. Table 3 illustrates the transfer FIELD for both assessments and interventions across all studies, with the effect sizes of the associated tests documented in Appendix A for reference. To assess side effects across different studies, diverse formulas were applied. In specific scenarios, conducting an analysis based on changes from baseline proves to be more efficient and potent than comparing final values. This approach eliminates a portion of between-person variability from the analysis, yielding more precise and robust results. By centering on changes within individuals over time, the impact of individual differences is minimized, enabling a more sensitive detection of treatment effects or interventions. Consequently, analyzing changes from baseline offers a clearer portrayal of the true treatment effect, augmenting the statistical power of the study (Cumpston et al., 2019). To compute the mean change in each group, the post-intervention mean is subtracted from the baseline mean.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"373\" height=\"54\"\u003e\u003c/p\u003e\n\u003cp\u003eThe standard deviation of the change from baseline for the experimental/control intervention was imputed using the following formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"609\" height=\"75\"\u003e\u003c/p\u003e\n\u003cp\u003eThe comparison of effect sizes across diverse studies and field domains involved using Stata (Version 14.2). This software was employed to weigh the effect sizes based on both the sample size and the number of tests conducted in each study. By integrating sample size and test quantity, the analysis took into consideration the variability and reliability of effect size estimates across studies. The weighting of effect sizes facilitated giving more weight to studies with larger sample sizes and more robust findings, while downweighing studies with smaller sample sizes or potentially less reliable results. Stata was utilized to execute transfer analysis procedures, considering study-specific weights. The software generated pooled effect sizes, offering a more accurate representation of the overall effect across the field domains. This approach allowed for a thorough comparison of effect sizes from different studies, enhancing the assessment of the combined effect. In this study, the interpretation of effect sizes utilized Hedges\u0026apos; g (Standardized Mean Difference for small sample sizes), with benchmarks categorizing small, medium, and large effect sizes set at approximately 0.2, 0.5, and 0.8, respectively.\u003c/p\u003e\n\u003cp\u003eThese benchmarks enabled a consistent and meaningful comparison of effect sizes across studies, facilitating the evaluation of the practical significance and impact of observed effects. The use of Hedges\u0026apos; g and standardized benchmarks provided valuable insights into the effectiveness and relevance of the interventions or treatments under investigation, enhancing the overall interpretability of the results.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 31 studies were included in this review, encompassing a participant pool of 887 individuals diagnosed with SLD. Among the participants, there were 804 children, 74 adolescents and 9 adults. The gender distribution comprised 259 females, 404 males, and 224 cases where gender was not specified. Subtypes of SLD were identified in 20 studies, with 504 participants classified with dyslexia, 23 with dyscalculia, and 360 with combined subtype conditoins. The SLD diagnosis was based on various criteria, including DSM-4 in 2 studies, DSM-5 in 4 studies, ICD 10 in 2 studies, QRS-L in 1 study, ABCA in 1 study, and not specified in 21 studies. Study designs included 14 randomized clinical trials (RCTs) and 17 non-randomized controlled (NRC) studies.\u0026nbsp;Among the included studies, 13 employed an active control group, while one remaining study (Flores-Gallegos et al., 2022) used waitlist controls. Active control interventions varied and included Neurofeedback (Azizi et al., 2018), non-action video game (Bertoni et al., 2019; Franceschini et al., 2013, 2017), visual training (C Gibert et al., 2023), reading training (Chenault et al., 2006; Lorusso et al., 2005, 2006), non-attentional training (Ren et al., 2023), and social skill training (Malboeuf-Hurtubise et al., 2019).\u003c/p\u003e\n\u003cp\u003eThe sample sizes in the included studies ranged from 9 to 64 participants (see Table 2 for details). Tables 3 and 4 outline the assessment tools used in the studies. Assessment procedures included two sessions (pre-test and post-test) in 26 studies and three sessions (pre-test, post-test, and follow-up) in 5 studies. Follow-up periods ranged from 2 months to 8 months. Assessments were categorized into neural, cognitive, and behavioral levels, as presented in Tables 3 and 4.\u003c/p\u003e\n\u003cp\u003eNeural assessments, exemplified by fMRI (functional magnetic resonance imaging) or electroencephalography, employed physiological measurements to assess neural activity. Cognitive assessments, such as the continuous performance test, entailed tasks designed to gauge the accuracy and/or speed of cognitive performance. Behavioral assessments utilized objective measures in the form of questionnaire-based evaluations to appraise behavioral performance. The evaluations in the studies included were conducted in diverse settings, including school (11 studies) and clinical settings (14 studies). Some studies employed evaluations in multiple settings, such as school and clinic (1 study). Concerning interventions, they were implemented in various settings as well, including School (5 studies) and clinical settings (11 studies). Similar to assessments, some studies executed interventions in multiple settings, such as home and clinic (1 study), and school and home (6 studies). In Tables 3 and 4, the behavioral and cognitive assessment tools are categorized based on the agent/responder (self-, parent-, teacher-, clinician-rating), objectivity, material (paper and pencil or computerized tasks), and the setting in which they were performed (clinic, home, or school).\u003c/p\u003e\n\u003cp\u003eTable 2. Demographic and diagnostic characteristics of studies\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthors, Year\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Design, Control\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (I, C), Age Mean (SD), Age Range, Gender (F:M), Education (Yr)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubtypes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csub\u003eDD, MLD\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlqarni \u0026amp; Hammad, 2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30 (15, 15), ns (ns), ns, 0:30, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAshkenazi \u0026amp; Henik, 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSG, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (9, 0), 24.7 (1.98), ns, 7:2, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0, 9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAzizi et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45 (15, 15), 8.53 (1.62), 7-10, 30:15, 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDSM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBeauchemin et al., 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSG, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34 (34, 0), 16.61 (ns), 13-18, 5:29, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBertoni et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (7, 7), 10.1 (1.6), ns, 6:8, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDSM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBertoni et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCO, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (7, 7), 8.93 (.99), ns, 4:10, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDSM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCaldani et al., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, SB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50 (25, 25), 9.65 (.35), 7.8-12, ns, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChenault et al., 2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20 (10, 10), 10.66 (.87), ns, 8:12, 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFacoetti et al., 2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24 (12, 12), 9.48 (ns), 7-9, 4:20, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFlores-Gallegos et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (6, 5), 7.9 (ns), 6.9-10.9, 4:7, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFranceschini et al., 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, SB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (10, 10), 9.8 (1.4), 7-13, ns, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFranceschini et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (16, 12), 10.27 (1.72), 7.8-14.3, 8:20, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGibert et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (24, 15), 9.04 (.11), ns, 18:21, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGuarnera \u0026amp; D\u0026rsquo;Amico, 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (7, 7), 9.42 (.4), 8-10, 6:8, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eABCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0, 14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u0026eacute;rez-Puelles et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSG, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (32,0), 8.97 (1.61), 6-12, 9:23, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQRS-L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeim et al., 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33 (7, 26), 10 (0.6), 8.7-11.2, ns, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHelland et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSG, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (16, 31), 8.78 (.26), 8-9, 6:10, 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJones \u0026amp; Finch, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSG, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (9,0), ns (ns), ns, ns, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLorusso et al., 2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (6, 6), 10.75 (2.46), 8-14, ns, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICD-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLorusso et al., 2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (14, 11), 9.8 (2.24), 7-15, 3:22, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICD-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalboeuf-Hurtubise et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSG, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (14, 0), 10.7 (1.1), 9-12, 8:6, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalboeuf-Hurtubise et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSG, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (14, 0), 10.7 (1.1), 9-12, 8:6, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalboeuf-Hurtubise et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (13, 10), ns (ns), 9-12, ns, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMeng et al., 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (18, 0), 10.34 (.78), ns, 10:26, 5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDSM-IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePedroli et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSG, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (10, 0), 10.6 (1.4), 9-12, 2:8, 5 5.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePeters et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, SB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64 (23, 22, 19), 10.53 (.99), 8-13, ns, 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDSM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRen, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, DB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45 (30, 15), 10.37 (1.31), ns, 9:36, 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSolan et al., 2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30 (15, 15), 11.3(.3), ns, ns, 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSolan et al., 2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSG, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (16, 0), ns (ns), ns, 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVeysi et al., 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40 (20, 20), 13.85 (2.23), ns, 40:0, ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDSM-IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eZhao et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCG, P, OL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40 (20, 20), 10.06 (1.5), ns, 11:29, 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40, 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003e1. CG: Control Group, CO: Crossover, DB: Double Blind, OL: Open Label, P: Parallel, SB: Single Blind, SG: Single Group, 3. DD: Developmental Dyslexia, MLD: Mathematical Learning Disorder, 2. ADCA: abilit\u0026agrave; di calcolo aritmetico, QRS-L: Questionnaire of Risk Signs and Learning Problems\u003c/p\u003e\n\u003cp\u003eThe interventions administered in the studies covered both the behavioral aspect, specifically attentional state training (n=7), and the cognitive aspect, concentrating on attentional process training (n=24). The duration of these intervention programs varied among studies, with attentional state training spanning from 4.16 to 24 hours in 7 studies, and attentional processing training ranging from 0.17 to 24 hours in 24 studies (refer to Table 6 for details). Table 5 provides a description and categorization of intervention program properties based on administration (clinician-, parent-, teacher-, or self-administered), objectivity (objective task-based intervention or subjective intervention), and material (computerized, paper and pencil, educational, or motoric). These characteristics of assessment tools and intervention programs were employed in our framework to explore various domains of transfer.\u003c/p\u003e\n\u003cp\u003eThe findings of the studies included in the analysis were evaluated within the framework of five transfer domains. In the realm of function, behavioral and cognitive interventions were interpreted differently. In the context of behavioral intervention, AST, the transference of training effects to SLD symptoms was categorized as near transfer. Conversely, the extension of training effects to broader performance aspects outside the intervention\u0026apos;s focus, such as anxiety, was deemed as far transfer. Additionally, improvements in mindfulness or attentional states were noted as having no discernible transfer.\u003c/p\u003e\n\u003cp\u003eWithin the scope of cognitive intervention, APT, the categorization of near and far transfer hinged on the functional likelihood of cognitive functions. Attention, as a foundational function with diverse domains, demonstrated no transfer effect when targeted for enhancement across various attentional levels, such as focused, sustained, selective, shifting, and divided attention. The transfer of training effects to executive functions, encompassing working memory, inhibitory control, and cognitive flexibility, was considered as near transfer. In contrast, the transfer to non-executive domains like social cognition and emotional processing was identified as far transfer. The effect sizes of transfer domains in the included studies are detailed in Table 7. We classify transfer effects according to the magnitude of their effect sizes as follows: a transfer effect with an effect size below 0.2 is deemed small, an effect size ranging from 0.21 to 0.50 is characterized as medium, and an effect size surpassing 0.51 is designated as large.\u003c/p\u003e\n\u003cp\u003eTable 3. Properties of behavioral assessments in the included studies\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasurement (abbreviation; developer)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure(s)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProperties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBarratt\u0026apos;s Impulsiveness Scale (BIS-11; Patton et al., 1995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eImpulsivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlphabet Writing Task(AWT; Puranik et al., 2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWriting\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWechsler Individual Achievement Test (WIAT; Wechsler, 1992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWriting\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGray Oral Reading Test (GORT; Wiederholt \u0026amp; Bryant, 1992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAbilit\u0026agrave; di calcolo aritmetico (ADCA; Lucangeli et al., 1998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMathematics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esubitizing and counting (SC; Ashkenazi \u0026amp; Henik, 2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMathematics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Eacute;valuation de la Lecture en FluencE (ELFE; Erika Godde, Marie-Line Bosse, 2021)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eReading Test (RT; Cornoldi \u0026amp; Colpo, 1985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEvaluaci\u0026oacute;n neuropsicol\u0026oacute;gica infantil (ENI-2; Matute Esmeralda et al., 2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading, writing, attention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePiers Harris self-concept scale (PH; Huebner, 1994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eself-perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBiomechanical tasks (BM; Olree \u0026amp; Vaughan, 1995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMotor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGates-MacGinitie Reading Test (GMG; W. MacGinitie, 1989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYork Assessment of Reading for Comprehension (YARC; Martin, 2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eComprehensive Test of \u0026nbsp; \u0026nbsp; Phonological Processing (CTOPP-2; Tennant, 2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePhonological awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003echaracter-list reading task (CLRT; Zhao et al., 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esentence verification task (SVT; van den Boer et al., 2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSentence reading test (SRT; Zhao et al., 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esingle-character reading tasks (SCRT; Zhao et al., 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSocial Skills Rating System (SSRS; Gresham, F. M., \u0026amp; Elliot, 1990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInterpersonal skill\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eState\u0026ndash;Trait Anxiety Inventory (STAI; Gonzalez-reigosa \u0026amp; Io 1971)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePsychological State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAttitudinal questions( AQ; J. D. Beauchemin, 2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePsychological State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Alphanumeric RAN Task ( NARANT; Mascheretti et al., 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhoneme-Blending Task (PBT; Franceschini et al., 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhonology Basiskompetenzen f\u0026uuml;r Lese-Rechtschreibleistungen (BAKO; Marie-Line Bosse , Marie Jos\u0026egrave;phe Tainturier, 2007)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePhonological awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRapid Automized Naming (RAN;\u0026nbsp;Hugdahl, 1995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlasgow Anxiety Scale for individuals with Intellectual Disabilities (GAS-ID; Mindham \u0026amp; Espie, 2003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePsychological State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognitive and Affective Mindfulness Scale\u0026mdash;Revised (CAMS-R; Feldman et al. 2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePsychological State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBatteria per la Valutazione della Dislessia e DisortograWa Evolutiv (BVDDE; (G. Sartori, R. Job, 1995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eText reading (TR;\u0026nbsp;Cornoldi \u0026amp; Colpo, 1985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSingle word/non-word reading (SW/NWR; G. Sartori, R. Job, 1995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWords\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSpelling tests (ST; G. Sartori, R. Job, 1995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWriting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhonemic Awareness (PA;\u0026nbsp;Cossu et al.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePhonological awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNeed Satisfaction (NS; Savard et al., 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAutonomy, competence, relatedness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBehavior Assessment System for Children, Second Edition (BASC; Reynolds, 1997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInternalized Symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMindfulness Measure (MM; Malboeuf-Hurtubise, Lacourse, Taylor, et al., 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMindfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStandardized Chinese Character Recognition Test( SCCRT;\u0026nbsp;Liao et al., 2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGates-MacGinitie Reading Comprehension Normal Curve Equivalence (GMNCE;\u0026nbsp;MacGinitie et al., 43AD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWoodcock-Johnson Word Attack (WJWA;\u0026nbsp;Mather \u0026amp; Jaffe., 2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAffective Control Scale (ACS; Melka et al., 2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAnger, depression, anxiety, positive affect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYoung Schema Questionnaire-Short Form (YSQ- SF;\u0026nbsp;Young, 1998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMaladaptive schemas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: 1. A: agent, C: clinician, P: parent, S: self-administered, T: teacher; 2. O: objectivity, O: objective, S: subjective, 3. M: material, C: computerized, P: paper and pencil; 4. S: setting, C: clinic, H: home, S: school, ADL: activity daily living, Symp: symptoms\u003c/p\u003e\n\u003cp\u003eTable 4. Properties of cognitive assessments in the included studies\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasurement (abbreviation, developer)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure(s)\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProperties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003csup\u003e4\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eContinuous Performance Test \u0026nbsp;(CPT; Homack et al., 2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSustained attention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDelis\u0026ndash;Kaplan Executive Function System (DKEFS; Delis 2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention, inhibition, switching, letter Fluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWorking Memory Tasks (WMT, Daneman, 1980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWorking memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAttenzione e Concentrazione (AC; Di Nuovo, Santo, 2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention Networks Test and Interactions (ANT-I; Callejas et al., 2004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVisual attentional span (VAS; Marie-Line Bosse , Marie Jos\u0026egrave;phe Tainturier, 2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCovert orienting of visual attention (COVA; Facoetti et al., 2003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVisual attention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTest Of Variables of Attention (TOVA; Greenberg \u0026amp; Waldmant, 1993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSelective attention and inhibition.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTest of Visual Perceptual Skills (TVPS; Martin, 2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVisual skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognitive Assessment System (CAS; Naglieri, Jack A., 1997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention KITAP (AK; Marie-Line Bosse , Marie Jos\u0026egrave;phe Tainturier, 2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAttentional Blink Task (ABT; Lacroix et al., 2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePosner\u0026apos;s task (PT; Posner et al., 1980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMagnocellular temporal processing tasks (MTPT; Peters et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTemporal processing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTheory of visual attention based assessment (TVABA; Habekost, 2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWorking memory, attention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVisual 1-back task (VBT; Zhao et al., 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVisual Attention Span\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVisual Search Task (VST; Bertoni et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVisual attention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCrowding task (CT; Yeshurun \u0026amp; Rashal, 2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVisual attention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFocused and distributed spatial attention (FDSA; Marie-Line Bosse , Marie Jos\u0026egrave;phe Tainturier, 2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCross-modal Attention Task (CAT; Petersen \u0026amp; Posner, 2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAuditory-Phonological Working Memory (APWM; Franceschini et al., 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWorking memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention Shifting (AS; Franceschini et al., 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVisual, auditory, audio-visual processing and cross-sensory attentional shifting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDigit Span (DS; Chenault et al., 2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWorking Memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eForm-Resolving Field (FRF; Geiger et al., 1992; Lorusso et al., 2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVisual perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMemory (M; Reynolds, 1997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVerbal memory, working memory, long term memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCoherent Motion Threshold (CMTI; Patel et al., 2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAttention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e 1. CF: cognitive flexibility, EF: executive function, Lang: language: IC: inhibitory control, RDM: risky decision making, STM: short-term memory, WM: working memory; 2. A: agent, S: self-administered; 3. O: objectivity, objective; 4. M: material, C: computerized, P: paper-pencil; 4. S: Setting, C: Clinic, H: Home, S: School\u003c/p\u003e\n\u003cp\u003eTable 5. Description of interventions and properties\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Abbreviation; Developer)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Respective included studies)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProperties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003csup\u003e4\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMindfulness based cognitive therapy (MBCT; Gilbert \u0026amp; Procter, 2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA program that combines mindfulness meditation with cognitive-behavioral techniques. It integrates mindfulness practices, cognitive therapy principles, and self-compassion strategies, encompassing training in body scan, breathing exercises, meditation, as well as fostering accepting and nonjudgmental focus and coping skills.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePerceptual Accuracy-Visual Efficiency (PAVE; Groffman, S., \u0026amp; Press, 1989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThis intervention encompasses tasks aimed at developing attention including detection, perceptual accuracy, visual search, visual span, visual scan, and guided reading tasks.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAttentional Training (AT; Habekost, 2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAn attentional training regimen involves tasks such as the visual rapid discrimination task, visual short-term memory span task, and judgment of target orientation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003eFruit Ninja (https://fruitninja.com)\u003c/pre\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEngaging in a task involving slicing fruits by moving the cursor on the screen while holding down the left button was part of the activity. In a different segment, eye movements were tracked using an infrared camera to control the cursor, with the goal of improving dynamic visual attention through precise and well-timed eye movements.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNeuro VR (Pedroli et al., 2017)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIn the virtual reality environment, attentional tasks involve responding to a dynamically changing target among 3D objects on a blackboard, reacting to the letter \u0026quot;G\u0026quot; following the prime letter \u0026quot;A,\u0026quot; and identifying a target color associated with a specific category among four colors on the blackboard while listening to a story.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVisual Texture Discrimination Training (VTDT; Meng et al., 2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIn this visual perception experiment, participants fixated on a randomly rotated \u0026quot;T\u0026quot; or \u0026quot;L\u0026quot; presented at the bottom of a texture stimulus to indicate the fixation letter (\u0026quot;T\u0026quot; or \u0026quot;L\u0026quot;) and then specify the target texture orientation (horizontal or vertical). Correct responses required accuracy in both the letter and target texture judgments, with no feedback given to participants.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFlash Word Training (FWT; Masutto; Fabbro, 1995)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA computerized program facilitates ocular fixation monitoring by tracking a luminous dot oscillating between the top and bottom of the screen at an adjustable speed. The word is displayed only when the child accurately clicks the mouse at the precise moment the dot crosses the central target.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDichotic Listening (DL; Bless et al., 2013)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParticipating in selective auditory attention training within a dichotic listening paradigm entailed utilizing constant vowel-syllables (/ba/, /da/, /ga/, /pa/, /ta/, /ka/), featuring six homonym pairs presented simultaneously to both ears. Displayed on a touch screen, subjects were directed to promptly select the correct syllable under forced right or left ear conditions, as well as in situations with no forced direction.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCogniPlus (cogniplus co.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA computerized intervention incorporating tasks for alertness, visual-spatial attention, selective attention, and divided/focused attention training.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCeleco (Werth, 2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA reading-specific attention training method involving the reorientation of attention during the systematic scanning of word fragments, aiming to promote smooth and targeted gaze movements.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAttention Training (AT; Di Nuovo, 2000)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA comprehensive set of attention training tasks encompassing simple and choice reaction time, visual, visuo-spatial, and auditory selectivity, digit span, divided attention, resistance to distraction, and attentive shifting.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMoveR (Gibert et al., 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA series of tasks within a virtual reality environment, including \u0026quot;Read in Motion\u0026quot; to enhance visual discrimination, attentional span, and promote focal visual attention; \u0026quot;Battlerace\u0026quot; to improve saccades and motor coordination; \u0026quot;Jump in Words\u0026quot; to enhance spatial orientation; and \u0026quot;Vergence Movements\u0026quot; to refine visual coordination.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeatgames (https://beatsaber.com)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA set of gross motor coordination movements in a virtual reality setting where a specific musical rhythm by striking color cubes (red or blue) in a prescribed order and position to enhance visual attention, visual and motor coordination, and balance.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGrafoTami (Oculus, 2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAn immersive virtual reality game involves object identification based on clue words across levels. Progressing through levels includes matching words, identifying corresponding phrases, and completing sentences, with complexity scaling based on syllables and sound structure. The interactive element incorporates a ball gun, where players read a red-colored clue word or sentence on a virtual wall, locate the corresponding word or object, and skillfully throw a ball at it.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVisual Hemisphere-Specific Stimulation (VHSS; Bakker, Bouma, \u0026amp; Gardien, 1990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA series of ocular fixation tasks involves tracking a dot oscillating in the left or right visual field and responding when the dot reaches a central target. The complexity of the task heightens as strings of letters (words) become progressively more challenging in terms of length and frequency of use.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAttention Training (AT; Thomson et al., 2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA comprehensive attention training program comprises exercises focused on various aspects, including understanding and retaining auditory information and instructions, response speed, categorization of visual and auditory materials using verbal labels, visual search, motor response to visual and auditory targets, multitasking with evaluation of multiple categories, flexible task-switching, and sustaining focus on target stimuli amid auditory and visual distractions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVisual Attention Training (VAT; Caldani et al., 2020)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA comprehensive attention training program comprises exercises focused on various aspects, including understanding and retaining auditory information and instructions, response speed, categorization of visual and auditory materials using verbal labels, visual search, motor response to visual and auditory targets, multitasking with evaluation of multiple categories, flexible task-switching, and sustaining focus on target stimuli amid auditory and visual distractions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVisual Attention Span Training (VAST; Qian \u0026amp; Bi, 2015; Zhao et al., 2019)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAn array of attention training tasks, encompassing activities such as length estimation, digit cancelling, and visual search exercises.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRayman Raving Rabbids (RRR; IGN co)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAn assortment of games, including shooting plungers at rhythm-dancing rabbits and engaging in various visual and auditory challenges.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003eCall of Duty (infinityward)\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA series of challenges requiring rapid responses, mastery of weapon accuracy, exploration of new environments, and adept handling of multiple targets, all within the epic World War II battlefield as experienced through the perspectives of both civilians and soldiers.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: 1. A: agent, C: clinician, P: parent, S: self-administered, T: teacher; 2. O: objectivity, O: objective, S: subjective, 3. M: material, C: computerized, Me: Mental, P: paper and pencil; 4. S: setting, C: clinic, H: home, S: school\u003c/p\u003e\n\u003cp\u003eTable 6. Properties of assessments and intervention in the included studies\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor, Year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003csup\u003e1\u0026nbsp;\u003c/sup\u003e(Agent\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSetting\u003csup\u003e3\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eCharacteristic\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e4\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e:Name\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSetting\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime\u003csup\u003e6\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlqarni \u0026amp; Hammad, 2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMBCT (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOP:\u003c/u\u003e BIS-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAshkenazi \u0026amp; Henik, 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCall of Duty (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC:\u003c/u\u003e ANT-I \u0026amp; SC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAzizi et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAPT(S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC:\u003c/u\u003e CPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBeauchemin et al., 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMBCT (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOP\u003c/u\u003e: AQ \u0026amp; STAI, \u003cu\u003eTOP:\u003c/u\u003e SSRS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBertoni et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRRR(S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: CT, \u003cu\u003eSOP\u003c/u\u003e: RT,\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBertoni et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRRR (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOP:\u003c/u\u003e NARANT, RT, VST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCaldani et al., 2020a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAVAT (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC:\u003c/u\u003e ELFE, ET \u0026amp; VAS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChenault et al., 2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAT (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: AWT, DKEFS, GORT, WIAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFacoetti et al., 2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVHSS (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOP:\u003c/u\u003e COVA \u0026amp; RT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFlores-Gallegos et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeatgames (S), GrafoTami (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC:\u003c/u\u003e TOVA, \u003cu\u003eSOM:\u003c/u\u003e BMT, \u003cu\u003eSOP:\u0026nbsp;\u003c/u\u003eENI-2, PHSCS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFranceschini et al., 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRRR (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: CAT \u0026amp; FDSA, \u003cu\u003eSOP\u003c/u\u003e: PBT, RT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePPF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFranceschini et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRRR (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: AS \u0026amp; FDSA, \u003cu\u003eSOP\u003c/u\u003e: APWM, RT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGibert et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMoveR (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: TVPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGuarnera \u0026amp; D\u0026rsquo;Amico, 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAT (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: AC, \u003cu\u003eSOP\u003c/u\u003e: ABCA, WMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u0026eacute;rez-Puelles et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVideoGame (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: CPT, SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeim et al., 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCogniPlus (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: AK, RT \u0026amp; PB, \u003cu\u003eCOD\u003c/u\u003e: fMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHelland et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDL (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: DS, RAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJones \u0026amp; Finch, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMBCT (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOP\u003c/u\u003e: GAS-ID, CAMS-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLorusso et al., 2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVHSS (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: FRF, \u003cu\u003eSOP\u003c/u\u003e: BVDDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLorusso et al., 2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVHSS (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOP\u003c/u\u003e: M, ST, SW/NWR, TPA, TR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalboeuf-Hurtubise et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMBCT (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOP\u003c/u\u003e: MM \u0026amp; BASC-II, \u003cu\u003eTOP\u003c/u\u003e: BASC-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalboeuf-Hurtubise et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMBCT (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOP\u003c/u\u003e: NS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalboeuf-Hurtubise et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMBCT (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOP\u003c/u\u003e: BASC-II, NS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePPF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMeng et al., 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVTDT (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: RT, \u003cu\u003eSOP\u003c/u\u003e: SCCRT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePPF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePedroli et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeuro VR (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: ABT, PT, RT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePeters et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFruit Ninja (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: ET \u0026amp; MTPT, \u003cu\u003eSOP\u003c/u\u003e: CTOPP-2, YARC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRen, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAT (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: SVT \u0026amp; TVABA, \u003cu\u003eSOP\u003c/u\u003e: CLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePPF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSolan et al., 2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePAVE (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: CAS, \u003cu\u003eSOP\u003c/u\u003e: GMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSolan et al., 2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePAVE (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: CMTI, GMNCE, GORT, WJWA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVeysi et al., 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMBCT (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOP\u003c/u\u003e: ACS, YSQ-SF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eZhao et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVAST (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cu\u003eSOC\u003c/u\u003e: VBT, \u003cu\u003eSOP\u003c/u\u003e: SCRT, SRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePPF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e1. AT: Attention Training, AVAT: Auditory-Visual Attention Training, DL: Dichotic Listening, FWT: Flash Word Training, MBCT: Mindfulness based cognitive therapy, PAVE: Perceptual Accuracy-Visual Efficiency, RRR: Rayman Raving Rabbids, VAST: Visual Attention Span Training, VAT: Visual Attentional Training, VHSS: Visual Hemisphere-Specific Stimulation, VTDT: Visual Texture Discrimination Training; \u003cstrong\u003e2.\u003c/strong\u003e C: Clinician, P: Parent, S: Self, T: Teacher; \u003cstrong\u003e3.\u003c/strong\u003e C: Clinic, H: Home, S: School; \u003cstrong\u003e4.\u003c/strong\u003e Clinician, P: Parent, S: Self-administered, T: Teacher /O: Objective, S: Subjective / C: Computerized, P: Paper and pencil; \u003cstrong\u003e5.\u003c/strong\u003e ABCA: Abilit\u0026agrave; di calcolo aritmetico, ABT: Attentional Blink Task, AC: Attenzione e Concentrazione, ACS: Affective Control Scale, AK: Attention KITAP, ANT-I: Attention Networks Test and Interactions, APWM: Auditory-phonological working memory, AS: Attention Shifting, AQ: Attitudinal Questions, AT: Attention Test, AWT: Alphabet Writing Task, BASC-II: Behavior Assessment Scale for Children, BIS-11: Barratt\u0026apos;s Impulsiveness Scale, BMT: Biomechanical tasks, BVDDE: Batteria per la Valutazione della Dislessia e DisortograWa Evolutiv, CAS: Cognitive Assessment System, CAT: Cross-modal Attention Task, CLRT: character-list reading task, CMTI: Coherent Motion Threshold, COVA: Covert Orienting of Visual Attention, CPT: Continuous Performance Test, CT: Crowding Task, CTOPP-2: Comprehensive Test of Phonological Processing, DKEFS: Delis\u0026ndash;Kaplan Executive Function System, DS: Digit Span, ELFE: \u0026Eacute;valuation de la Lecture en FluencE, ENI-2: Evaluaci\u0026oacute;n neuropsicol\u0026oacute;gica infantile, ET: Eye Tracking, FDSA: Focused and Distributed Spatial Attention, fMRI: Functional magnetic resonance imaging, FRF: FRF: Form-Resolving Field, CAMS-R: Cognitive and Affective Mindfulness Scale-Revised, GAS-ID: Glasgow Anxiety Scale for individuals with Intellectual Disabilities, GMG: Gates-MacGinitie Reading Test, GMNCE: Gates-MacGinitie Reading Comprehension Normal Curve Equivalence, GORT: Gray Oral Reading Test, M: Memory, MM: Mindfulness Measure, MTPT: Magnocellular temporal processing tasks, NARANT: Non-Alphanumeric Rapid Automized Naming Task, NS: Need Satisfaction, PA: Phonemic Awareness, PB: Phonology BAKO, PBT: Phoneme-Blending Task, PHSCS: Piers Harris self-concept scale, PT: Posner\u0026rsquo;s task, RAN: Rapid Automized Naming, RT: Reading Test, SA: Sustained Attention, SC: Subitizing and counting, SCCRT: Standardized Chinese Character Recognition Test, \u0026nbsp;SCRT: single-character reading tasks, SRT: Sentence reading test, SSRS: Social Skills Rating System, ST: Spelling Test, STAI: The State\u0026ndash;Trait Anxiety Inventory, SVT: sentence verification task, SW/NWR: Single Word/Non Word Reading, TOVA: Test of Variables of Attention, TR: Text Reading, TVABA: Theory of visual attention based assessment, TVPS: Test of visual perceptual skills, VAS: Visual Attention Span, VBT: Visual 1-back task, WIAT: Wechsler Individual Achievement Test, VST: Visual Search Task, WJWA: Woodcock-Johnson Word Attack, WMT: Working Memory Tasks, YARC: York Assessment of Reading for comprehsion, YSQ- SF: Young Schema Questionnaire-Short Form\u003cstrong\u003e; 6.\u003c/strong\u003e PP: Pre-test and Post-test, PPF: Pre-test, Post-test and follow-up\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn detail, regarding functional transfer, 6 studies demonstrated no transfer (Alqarni \u0026amp; Hammad, 2021; Azizi et al., 2018; Heim et al., 2015; P\u0026eacute;rez-Puelles et al., 2022; Helland et al., 2018; Malboeuf-Hurtubise et al., 2018), 4 studies exhibited small transfer effects (Ashkenazi \u0026amp; Henik, 2012; Bertoni et al., 2021; Pedroli et al., 2017; Zhao et al., 2019), 6 studies unveiled medium transfer effects (Chenault et al., 2006; Flores-Gallegos et al., 2022; Gibert et al., 2023; Guarnera \u0026amp; D\u0026rsquo;Amico, 2014; Ren, 2023; Solan et al., 2004), and 15 studies showcased large transfer effects (Beauchemin et al., 2008; Bertoni et al., 2019; Caldani, Gerard, et al., 2020; Facoetti et al., 2003; Franceschini et al., 2013; Franceschini et al., 2017; Jones \u0026amp; Finch, 2020; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2019; Meng et al., 2014; Peters et al., 2021; Solan et al., 2003; Veysi et al., 2015).\u003c/p\u003e\n\u003cp\u003eFor implemental transfer, 11 studies demonstrated no transfer effect(Alqarni \u0026amp; Hammad, 2021; Ashkenazi \u0026amp; Henik, 2012; Caldani, Gerard, et al., 2020; Chenault et al., 2006; Gibert et al., 2023; P\u0026eacute;rez-Puelles et al., 2022; Heim et al., 2015; Malboeuf-Hurtubise et al., 2018; Pedroli et al., 2017; Solan et al., 2004; Zhao et al., 2019), 3 studies exhibited small transfer effects (Bertoni et al., 2021; Guarnera \u0026amp; D\u0026rsquo;Amico, 2014; Ren, 2023), 4 studies unveiled medium transfer effects (Flores-Gallegos et al., 2022; Franceschini et al., 2017; Helland et al., 2018; Malboeuf-Hurtubise et al., 2017), and 13 studies showcased large transfer effects (Azizi et al., 2018; Beauchemin et al., 2008; Bertoni et al., 2019; Facoetti et al., 2003; Franceschini et al., 2013; Jones \u0026amp; Finch, 2020; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2019; Meng et al., 2014; Peters et al., 2021; Solan et al., 2003; Veysi et al., 2015) .\u003c/p\u003e\n\u003cp\u003eEcological transfer reveals that 25 studies presented no transfer effect (Alqarni \u0026amp; Hammad, 2021; Azizi et al., 2018; Bertoni et al., 2019; Bertoni et al., 2021; Caldani, Gerard, et al., 2020; Chenault et al., 2006; Facoetti et al., 2003; Flores-Gallegos et al., 2022; Franceschini et al., 2013; Franceschini et al., 2017; Gibert et al., 2023; Guarnera \u0026amp; D\u0026rsquo;Amico, 2014; P\u0026eacute;rez-Puelles et al., 2022; Heim et al., 2015; Helland et al., 2018; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2018; Meng et al., 2014; Pedroli et al., 2017; Peters et al., 2021; Ren, 2023; Solan et al., 2003; Solan et al., 2004; Zhao et al., 2019\u003cs\u003e)\u003c/s\u003e. Furthermore, 2 studies uncovered moderate transfer effects (Ashkenazi \u0026amp; Henik, 2012; Malboeuf-Hurtubise et al., 2017) , and 4 studies illustrated substantial transfer effects (Beauchemin et al., 2008; Jones \u0026amp; Finch, 2020; Malboeuf-Hurtubise et al., 2019; Veysi et al., 2015).\u003c/p\u003e\n\u003cp\u003eFor the level domain, we observe that 10 studies failed to demonstrate any transfer effect (Alqarni \u0026amp; Hammad, 2021; Gibert et al., 2023; Guarnera \u0026amp; D\u0026rsquo;Amico, 2014; P\u0026eacute;rez-Puelles et al., 2022; Heim et al., 2015; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2017; Solan et al., 2003; Veysi et al., 2015), whereas 5 studies exhibited minor transfer effects (Beauchemin et al., 2008; Franceschini et al., 2017; Malboeuf-Hurtubise et al., 2019; Pedroli et al., 2017; Solan et al., 2004). In addition, 3 studies revealed moderate transfer effects (Bertoni et al., 2021; Flores-Gallegos et al., 2022; Ren, 2023), and 13 studies showcased significant transfer effects (Ashkenazi \u0026amp; Henik, 2012; Azizi et al., 2018; Bertoni et al., 2019; Caldani, Gerard, et al., 2020; Chenault et al., 2006; Facoetti et al., 2003; Franceschini et al., 2013; Helland et al., 2018; Jones \u0026amp; Finch, 2020; Malboeuf-Hurtubise et al., 2018; Meng et al., 2014; Peters et al., 2021; Zhao et al., 2019 ).\u003c/p\u003e\n\u003cp\u003eFor the durability, it\u0026apos;s evident that 28 studies did not show any transfer effects (Alqarni \u0026amp; Hammad, 2021; Ashkenazi \u0026amp; Henik, 2012; Azizi et al., 2018; Beauchemin et al., 2008; Bertoni et al., 2019; Bertoni et al., 2021; Caldani, Gerard, et al., 2020; Chenault et al., 2006; Facoetti et al., 2003; Flores-Gallegos et al., 2022; Franceschini et al., 2013; Franceschini et al., 2017; Gibert et al., 2023; Guarnera \u0026amp; D\u0026rsquo;Amico, 2014; P\u0026eacute;rez-Puelles et al., 2022; Heim et al., 2015; Helland et al., 2018; Jones \u0026amp; Finch, 2020; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2017; Malboeuf-Hurtubise et al., 2018; Pedroli et al., 2017; Peters et al., 2021; Solan et al., 2003; Solan et al., 2004; Veysi et al., 2015; Zhao et al., 2019). In addition, 1 studies revealed moderate transfer effects (Ren, 2023), and 2 studies showcased significant transfer effects (Malboeuf-Hurtubise et al., 2019; Meng et al., 2014).\u003c/p\u003e\n\u003cp\u003eIn sum, for all FIELD\u0026rsquo;s domains, we find that 2 studies did not yield any transfer effects (Alqarni \u0026amp; Hammad, 2021; P\u0026eacute;rez-Puelles et al., 2022), while 10 studies exhibited minor transfer effects (Ashkenazi \u0026amp; Henik, 2012; Azizi et al., 2018; Bertoni et al., 2021; Chenault et al., 2006; Guarnera \u0026amp; D\u0026rsquo;Amico, 2014; Heim et al., 2015; Helland et al., 2018; Pedroli et al., 2017; Solan et al., 2004; Zhao et al., 2019). Moreover, 9 studies unveiled moderate transfer effects (Caldani, Gerard, et al., 2020; Flores-Gallegos et al., 2022; Franceschini et al., 2013; Franceschini et al., 2017; Gibert et al., 2023; Jones \u0026amp; Finch, 2020; Malboeuf-Hurtubise et al., 2017; Malboeuf-Hurtubise et al., 2019; Ren, 2023), and 10 studies demonstrated significant transfer effects (Beauchemin et al., 2008; Bertoni et al., 2019; Facoetti et al., 2003; Lorusso et al., 2005; Lorusso et al., 2006; Malboeuf-Hurtubise et al., 2018; Meng et al., 2014; Peters et al., 2021; Solan et al., 2003; Veysi et al., 2015).\u003c/p\u003e\n\u003cp\u003eTable 7. The effect sizes of included studies in the FIELD\u0026rsquo;s domains\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor (Year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s D (95% Confidence Interval)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlqarni \u0026amp; Hammad, 2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAshkenazi \u0026amp; Henik, 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.12 (0, .24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.33 (.11, .55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.3 (1.2, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.11 (-.02, .25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAzizi et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.71 (.37, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.89 (.23, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.14 (-.01,.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBeauchemin et al., 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.3 (1.2, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.3 (1.2, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3 (1.2, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.04 (-.02, .1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.04 (.84, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBertoni et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.89 (.23, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.89 (.23, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.85 (.48, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.53 (.08, .98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBertoni et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.04 (-.02,.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.04 (-.02, .1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.28 (.16, .39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.02 (-.02, .07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCaldani et al., 2020a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.85 (.48, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.69 (3.23, 6.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.34 (.09, .58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChenault et al., 2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.28 (.16, .39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.75 (.63, .87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.11 (.02, .2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFacoetti et al., 2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.69 (3.23, 6.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.69 (3.23, 6.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.59 (.33, .86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.82 (1.53, 4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFlores-Gallegos et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.42 (.18, .67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.42 (.18, .67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.32 (.19, .46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.32 (.09, .55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFranceschini et al., 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.59 (.33, .86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.59 (.33, .86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.11 (.98, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.35 (.12, .59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFranceschini et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.6 (.33,.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.32 (.19, .46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.14 (-.04, .32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.25 (.08, .42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGibert et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.28 (.1, .45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.28 (.11, .44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGuarnera \u0026amp; D\u0026rsquo;Amico, 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.25 (.05, .45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.14 (-.04, .32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.11 (-.05, .26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u0026eacute;rez-Puelles et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeim et al., 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.19 (.06, .31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHelland et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.3 (.24, .36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.33 (.51, 2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.06 (.02, .1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJones \u0026amp; Finch, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.51 (.5,.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.51 (.5, .51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.51 (.5, .51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.69 (1.31, 2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.3 (.13, .47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLorusso et al., 2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.62 (.96, 2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.33 (.51, 2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.86 (.17, 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLorusso et al., 2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.69 (1.31, 2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.69 (1.31, 2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.02 (.63, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalboeuf-Hurtubise et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.44 (.32,.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.44 (.32, .56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.44 (.32, .56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.26 (.12, .41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalboeuf-Hurtubise et al., 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.81 (.97, 2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.2 (.63, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMalboeuf-Hurtubise et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.6 (.49,.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.6 (.49, .71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.6 (.49, .71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.12 (.02, .22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.6 (.49, .71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.48 (.35, .61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMeng et al., 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.81 (.97, 2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.81 (.97, 2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.88 (1.94, 3.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.56 (2.07, 3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.6 (1.01, 2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePedroli et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.12 (.02,.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.15 (.08, .23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.05 (-.02, .12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePeters et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.13 (3.27, 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.88 (1.94, 3.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.72 (.35, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.98 (1.19, 2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRen, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.38 (.3,.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.15 (.08, .23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.42 (.09, .74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.38 (.3, .46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.21 (.13, .29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSolan et al., 2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.72 (.35, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.74 (1.59, 1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.63 (.33, .93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSolan et al., 2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.42 (.09,.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.1 (.08, .12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.17 (-.05, .38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVeysi et al., 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.15 (2.96, 3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.15 (2.96, 3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.15 (2.96, 3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.89 (1.39, 2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eZhao et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.1 (.08, .12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.3 (1.2, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e.04 (.02, .06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverall\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.78 (.63, .93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.88 (.7, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.05 (.52, 1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.7 (.52, .88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.05 (.56, 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.37 (.29, .45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDL (I\u003csup\u003e2\u003c/sup\u003e, p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99.1%, \u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99.9%, \u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99.5%, \u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98%, \u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97.5%, \u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93.1%, \u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations. F: function, I: implements, E: ecology, L: level, D: durability, S: sum of all domains\u003c/p\u003e\n\u003cp\u003eTable 8. Subgroup classification of transfer effect size\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable dir=\"rtl\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eHeterogeneity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eNumber\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eof Studies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eHedges\u0026apos; g\u0026nbsp;\u003cbr\u003e\u0026nbsp;(CI %95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eGroups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003ePotential Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e89.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.28 (.21, .35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp dir=\"LTR\"\u003eParticipants\u0026rsquo; Age (Yr.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e97.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e.99 (.12, 1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026ge;12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e94.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.39 (.27, .51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt; 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp dir=\"LTR\"\u003eIntervention Dose (Hour)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e91.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.52 (.32, .72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u0026ge; 12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e94.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e1.01 (.63, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp dir=\"LTR\"\u003eIntervention Level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.22 (.16, .29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eAPT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e94.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e1.01 (.63, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eCombined setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp dir=\"LTR\"\u003eIntervention Setting\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.11 (-.02, .25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eHome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.35 (.21, .49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eSchool\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e87.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.16 (0, .33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eClinic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e94.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e1.01 (.63, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eMental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp dir=\"LTR\"\u003eIntervention Material\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e87.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.29 (.19, .39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eComputerized\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.11 (.02, .2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003ePaper \u0026amp; pencil\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e80.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.2 (0, .39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eVirtual Reality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e93.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.32 (-.26, .9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e.053\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e.932\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e37.1%\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e0%\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e19\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e2\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003e.09 (.04, .14)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e.11 (.01, .21)\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003e.24 (.12, .36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eDyslexia\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eDyscalculia\u003c/p\u003e\n \u003cp dir=\"LTR\"\u003eSLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp dir=\"LTR\"\u003eSLD Subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e93.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e.37 (.29, .45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003eAll studies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations. F: function, I: implements, E: ecology, L: level, D: durability, S: sum of all domains, N: number of studies\u003c/p\u003e\n\u003cp\u003eTable 8 presents a detailed subgroup classification of transfer effects based on various potential factors. As illustrated in the table, adolescents and adults demonstrated a moderate transfer effect, whereas children exhibited a substantial transfer effect. In terms of intervention dosage, it is noteworthy that a longer duration of intervention yields a more favorable impact on transfer. Specifically, the transfer effect for shorter interventions (12 hours and below) is characterized as moderate, while interventions of a more extended duration (12 hours and beyond) result in a large transfer effect. Considering the intervention level, it is observed that AST displayed a significant transfer effect, whereas APT had a relatively minor impact. Examining intervention settings, combined settings emerged as the most effective, showcasing a large transfer effect compared to other settings. Additionally, school-based interventions revealed a moderate transfer effect, while home and clinic settings exhibited a smaller transfer effect. Turning to intervention materials, mental material demonstrated a substantial transfer effect. Computerized and combined interventions showed a moderate transfer effect, whereas paper-based and virtual reality interventions depicted a smaller transfer effect. Addressing the SLD subtype, it is noteworthy that the transfer effect in both subtypes was relatively similar and relatively small. However, the combined subtype revealed a medium effect size for transfer.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to undertake a thorough examination and analysis of the transferability of attention training in individuals with SLD. To assess transferability, we utilized the FIELD framework, encompassing function, implement, ecology, level, and duration. Our results highlighted the substantial impact of participant age, intervention method, level, and setting on the outcomes of transferability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe relevance of age.\u003c/strong\u003e The age factor significantly influences the effectiveness of interventions and investigations. Early intervention enhances outcomes for the majority of children with learning, attention, and cognition disorders (Pratt \u0026amp; Patel, 2007). When examining transferability, it consistently emerged that younger participants experienced a more prominent transfer effect compared to their older counterparts. A previous study on inhibitory control training in healthy individuals hints at the prospect of transferability to various cognitive functions in children, but this effect does not seem to extend to adults (Zhao et al., 2018). In another study involving inhibitory training with healthy adults and adolescents, positive outcomes were observed in both groups, indicating an enhanced inhibitory control. Nevertheless, the transfer pattern varied across age groups, with a more formative effect noted in the adolescents (WANG et al., 2020). The heightened potential for transferability in children and adolescents can be attributed to the greater plasticity of their developing brains (Hensch \u0026amp; Bilimoria, 2012; Park \u0026amp; Mackey, 2022). As children and adolescent brains undergo prolonged structural and functional reorganization, making them more responsive to training-induced changes (Dumontheil, 2016). However, it is important to note that the number of studies involving adults in our review was limited (only 3 out of 31), which restricts the strength and generalizability of any conclusions drawn about age-related differences. This sample imbalance should be taken into account when interpreting findings and highlights the need for future studies focusing on adult populations with SLD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe relevance of intervention dose.\u0026nbsp;\u003c/strong\u003eConcerning intervention dosage, it is important to highlight that a prolonged intervention duration has a more positive influence on transfer outcomes. To elaborate, shorter interventions (12 hours and below) exhibit a moderate transfer effect, whereas interventions lasting beyond 12 hours manifest a substantial transfer effect. Intensive intervention has been identified as a pivotal factor influencing the response to treatment in individuals with SLD (Reschly, 2014). A preceding study uncovered that the variance in the dosage and frequency of interactive book reading does not seem to impact word learning among children with developmental language disorders (Storkel et al., 2019). It is noteworthy to emphasize that attentional intervention represents a fundamental approach within the cognitive foundations of reading or mathematics, rather than merely repeating impaired skills in reading or calculation. A comparison between the outcomes of the current study and those of the earlier one underscores the significance of cognitive training over raw repetition for improvement. Importantly, the enhancement observed after skill training can be attributed to the compensation of impaired underpinnings with stronger components (Nejati, 2022). Considering the dosage aspect, it is essential to take into account the distribution of the intervention over time as another influencing factor. Previous studies have shown that distributed practice, as opposed to massed practice, results in enhanced learning outcomes. This holds true for both typically developing children (Haq \u0026amp; Kodak, 2015) and children with specific language impairment (Desmottes et al., 2017). In the present review, interventions were more evenly distributed in high-dose interventions compared to low-dose interventions, with 11.53 sessions over 6 weeks as opposed to 18.14 sessions over 8.2 weeks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe relevance of intervention level.\u003c/strong\u003e The findings indicate a substantial transfer effect for AST, contrasting with the relatively minor impact observed for APT. It is noteworthy that despite their methodological differences, APT employing objective cognitive tasks and AST utilizing subjective behavioral tasks, both interventions share a common focus on attention as a fundamental cognitive function. It is noteworthy to consider that the duration of AST was longer than APT, with 10 hours for the former and 40 hours for the latter. This discrepancy in duration should be taken into account when interpreting the results. While our current study did not incorporate research specifically focused on practicing academic skills for SLD, earlier studies have identified the benefits of breaking down these skills into their components for intervention. For example, a review study categorized occupational therapy interventions for SLD into \u0026quot;occupation-as-means\u0026quot; and \u0026quot;occupation-as-outcome\u0026quot; (Bray et al., 2021). The former employs occupation as the intervention in therapy, focusing on the \u0026apos;means,\u0026apos; while the latter views the intervention as the outcome or \u0026apos;end\u0026apos; of therapy (AOTA, 2021). This study discovered that interventions promoting self-management and utilizing occupation-as-means were particularly effective. Furthermore, one possible explanation for the greater transfer effect observed in AST is its emphasis on metacognitive and self-regulatory mechanisms. AST interventions often engage participants in mindful attention, emotion regulation, and context-sensitive behavior (Quaglia et al., 2019; Tang \u0026amp; Posner, 2009; Wadlinger \u0026amp; Isaacowitz, 2011), which may enhance the generalizability of learned skills to real-world settings. In contrast, APT tends to isolate cognitive components in a decontextualized manner (Nejati, 2021; Nejati \u0026amp; Derakhshan, 2024), which may limit the scope of transfer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe relevance of intervention setting.\u0026nbsp;\u003c/strong\u003eMoreover, interventions conducted within the school setting demonstrated a moderate transfer effect, whereas those implemented in home and clinic settings exhibited a comparatively smaller transfer effect. Previous research involving children with learning disabilities found that collaboration between therapists, parents, and the school not only strengthens but also enhances the effectiveness of interventions (Reschly, 2014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe relevance of intervention material.\u0026nbsp;\u003c/strong\u003eThe results suggest that mental materials exhibited a significant transfer effect. Computerized and combined interventions demonstrated a moderate transfer effect, while paper-based and virtual reality interventions revealed a small transfer effect.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eA previous meta-analysis indicated that computer-assisted interventions for mathematical training in students with learning disabilities did not demonstrate conclusive effectiveness, despite relatively large effect sizes (Seo \u0026amp; Bryant, 2009). A preceding study, comparing various intervention materials, indicates a widespread impact of cognitive strategy and direct instruction models in addressing academic difficulties in students with learning disabilities. However, the findings propose that the most impactful instructional approach is a combined model, as it yields the largest effect size (Swanson, 1999).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe relevance of intervention SLD Subtype.\u0026nbsp;\u003c/strong\u003eThe findings indicate that the transfer effect in both subtypes was notably comparable and relatively modest. Nevertheless, the combined subtype exhibited a moderate effect size for transfer. In a previous meta-analysis, it was uncovered that students with comorbid learning disorders exhibit distinct response patterns compared to those with specific mathematical disabilities. This discrepancy in response is contingent upon the nature of the mathematics intervention, suggesting the potential existence of a unique subtype. Conversely, students grappling with reading problems appear to respond uniformly to interventions, irrespective of whether their reading issues occur independently or in conjunction with mathematical disabilities (Fuchs et al., 2013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and future directions.\u003c/strong\u003e Several limitations should be acknowledged for this study. Firstly, the generalizability of the findings may be constrained by the specific characteristics of the participant pool and the selected interventions. The study primarily focused on attention training, and as such, the transferability insights may not be universally applicable to interventions targeting different cognitive domains. Notably, 17 out of the 31 included studies were non-randomized (Table 2), which may limit the strength of causal inferences drawn from the findings. In addition, the sample was predominantly composed of children and adolescents, with only 9 studies including adult participants, thereby restricting the generalizability of results to adult populations with SLD. Furthermore, several studies, particularly those involving mindfulness-based interventions, used pre\u0026ndash;post designs without control groups, raising concerns about potential placebo effects and limiting the ability to draw firm causal conclusions. Although a risk-of-bias assessment table was included to enhance transparency, future meta-analyses would benefit from stricter inclusion criteria prioritizing randomized controlled trials. Furthermore, the limited number of studies focusing on attentional state training (7 out of 31) restricts the strength of our conclusions regarding this intervention and limits the validity of direct comparisons with other training types. It is important to note that studies specifically targeting dyscalculia and dysgraphia were limited in number, which restricts the generalizability of our conclusions across all SLD subtypes. Future research should expand this line of inquiry to include other domains such as dys-orthographia and systematically evaluate the role of attention in diverse academic difficulties. These nuanced insights highlight the importance of tailoring interventions to specific age groups, utilizing diverse materials, and considering the unique characteristics of different SLD subtypes. Integrating these multifaceted considerations into future interventions for SLD holds the potential to significantly enhance cognitive training and skill improvement.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eConsistently, the study revealed a more pronounced transfer effect in younger participants, those with combined SLD, interventions with longer durations, and those utilizing mental materials compared to paper-based and computerized interventions. Furthermore, AST demonstrated superior transfer effects compared to APT. The collaborative efforts of researchers, educators, and therapists are deemed essential in developing targeted interventions that effectively address the diverse needs of individuals with learning disabilities. This collective approach ensures a more comprehensive understanding and application of effective strategies for the benefit of those navigating the challenges associated with SLD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no external funding. 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(2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Specific Learning Disabilities (SLD), FIELD’s model of transfer, attention training, systematic review","lastPublishedDoi":"10.21203/rs.3.rs-7853101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7853101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndividuals with Specific Learning Disabilities (SLD) experience cognitive impairments, including difficulties with attention. As attention constitutes a fundamental cognitive function that is trainable, its foundational nature makes it a suitable target for interventions aiming to extend training effects across other cognitive domains. This study systematically explores the transferability of attention training in individuals with SLD, guided by the FIELD framework of transfer, which stands for Function, Implement, Ecology, Level, and Durability of effects. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 31 original experiments were included in this study. Our transfer analysis, structured as a conceptual meta-analysis rooted in assessing effect sizes, sheds light on the pivotal influence of various factors, including age, SLD subtype, intervention dose, level, setting, and material. The findings indicate a heightened transfer effect in younger participants, those with combined SLD, interventions of extended durations, and those employing mental materials, in contrast to paper-based and computerized interventions. Additionally, attention state training exhibited superior transfer effects when compared to attention process training. These findings emphasize the crucial role of collaborative efforts among researchers, educators, and therapists for crafting targeted interventions that adeptly cater to the diverse needs of individuals with learning disabilities.\u003c/p\u003e","manuscriptTitle":"The Effectiveness of Attention Training in Specific Learning Disorders:A Systematic Review and Transfer Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 17:05:58","doi":"10.21203/rs.3.rs-7853101/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-06T10:47:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T18:29:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T10:48:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102794286061203884414116474942243772691","date":"2026-03-16T16:26:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53185665825468804585112296074410500716","date":"2026-03-15T18:19:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-18T12:41:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-17T16:04:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-04T11:48:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-31T14:31:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-31T14:25:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0b47723-6468-4293-9846-98e177edc8fa","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63137725,"name":"Health sciences/Health care"},{"id":63137726,"name":"Biological sciences/Psychology"},{"id":63137727,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-05T01:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 17:05:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7853101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7853101","identity":"rs-7853101","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}