Impact of Multimodal Cognitive Training on Cognitive Traits of Children: A Multicentric Interventional Study

Impact of Multimodal Cognitive Training on Cognitive Traits of Children: A Multicentric Interventional Study | 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 Research Article Impact of Multimodal Cognitive Training on Cognitive Traits of Children: A Multicentric Interventional Study Manjunatha R, Praveen Pankajakshan, Alphonsa Joseph, Gyan Kashyap, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-468572/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this article, we evaluate the hypothesis that a multimodal cognitive training (MCT) program, the Brighter Minds, can enhance certain inherent traits of a child and thus bring changes in the external behavior. For the study, 186 children (randomized to 93 each in intervention and control group) aged 10-15 years were enrolled from three different locations. Psychometric tests, parental/caregiver interviews and EEG (electroencephalography) tests were conducted before and after the program. Intervention group showed strong statistical significance for improvements in Mini Mental Status Examination (MMSE) (P<0.01) but no significance for Raven’s Standard Progrssive Matrices (SPM) or Susan Harter’s test. The parental/caregiver reported satistically significant improvements in focus (P<0.05), empathy (P<0.05), intuition (P<0.05), comprehension (P<0.05) and understanding of abstract concepts (P<0.05) for the intervention group. For the control, Power Spectral Density (PSD) of the baseline eyes-closed (EC) EEG recording, the spectrum below 20Hz exhibited the characteristic “1/f” spectral scaling of the power-law. This signature matches prior reported evidence in literature of those in wakeful state with EC. The intervention group EC PSD, however, exhibited a signature similar to those in a slow sleep state; reflective of the possible transfer effect of the training on other skills like relaxation. We used unsupervised learning methods with dice distance, on the psychometric and interview data, to show the effect of location and the exposure of a few control children to the program. Biological sciences/Neuroscience Biological sciences/Psychology Physical sciences/Mathematics and computing/Computational science cognitive traits behavior EEG multimodal cognitive training unsupervised learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction It is a long-held notion that learnings and impressions that are experienced at an early age, as a child, can have impact on the neuroanatomy and several neural structures for a long-term (Teicher et al. 2003 ). There are several evidences that link genetic, environment and life-style factors to the development or lack of development of the structure and functional aspects of the brain. The cognitive flexibility of a child or the ability to adapt the thoughts and behavior in response to changes in goals or the environment in life (Chevalier et al., 2012 ) could be attributed to these aspects. With changes in the environmental task demands, the working memory, attention, and response selection also changes accordingly. The degree of the change determines the degree of flexibility. Among children, flexibility develops rapidly during the preschool years and continues to improve across adolescence and young adulthood (Yeniad et al., 2013 , Titz and Karbach, 2014). Such cognitive skills, abilities (Hinshaw, 1994) and responses that are developed due to either or both of intrinsic and extrinsic dynamics in the early part of life are bound to prevail as traits throughout the lifespan. However, as the age advances, the cognitive ability to change in response to new extrinsic experiences reduces. The interplay of genes and in-utero experiences might be responsible for some children being born with hypersensitive behavior facilitation (Gray 1987) or an underactive behavioral inhibition system (Scarpal & Raine, 2000). It is thus necessary to develop a balanced approach to emotional, social, cognitive and language development for preparing children to success in school, and later on as adults in family, workplace and community (In Brief: The Science of Early Childhood Development, 2018). Cognitive Training The biopsychosocial (BPS) framework (Grinker RR Sr. 1964, Engel 1981 ), was the earliest to provide an integrative, comprehensive model to human development, health, and functioning. It looks at the interconnection between biology, psychology, and socio-environmental factors, to understand how these influence health, disease and wellness. It has now established itself as the status quo model for contemporary medicine. Engel declared that this model was developed to overcome the limitations and to bring contrast to the biomedical model which was prevalent at that time. It is the claim that the BPS model itself takes its roots from the general systems theory (GST). Critics of the BPS (Ghaemi 2009 , Benning 2015 and Bolton 2019 ) argue that the BPS model is very vague, eclectic and unfaithful to GST, and is not scientific in its approach. Another contention, related to the above subject, is that the GST itself might not be a right framework to address the complexity of the mind (McLaren 1998). These theories and models must be understood in the context of trying to understand or identify the root cause of observed effects such as behavior, disease or health. We acknowledge that it is difficult albeit sometimes impossible to determine the exact causal basis to some of these effects (Karunamuni 2020); because the cause-effect relationship and pathways might be multivariate with latent variables, and highly nonlinear pathways. There are alternative models proposed recently (Ghaemi SN 2009), which focus on the humanistic element to medical treatment, but we focus our attention on the psychological component of the BPS. The idea is to seek a psychological foundation for a behavior or behaviors. Cognitive Training (CT) aims at exploring the central idea that the neuronal structure and function can be changed by altering either the environment or experience or both. Some of the earliest results from literature (Douglas, V.I. et al. 1976 ) (Shaw, C., Lanius, R., & Doel, K.V., 1994) provide light along these directions. As an alternative to pharmaceutical interventions, parents are turning to CT for their children and adolescents to enhance cognitive abilities. Similarly, caregivers of elderly citizens are turning to CT for reducing the onset of age related dementia. Studies have reported the effects of CT on changes in cognition and behavior, across multiple disciplines: developmental psychology, social psychology, clinical psychology, educational psychology, and cognitive psychology (Jedlicka, 2017 ). Numerous techniques conceptualize how children's cognitive ability can be enhanced through CT (Jedlicka, 2017 ; Madhavi & Jayanna, 2019). In Rabipour, Sheida & Raz, Amir. 2012, the authors review published literature on CT for Attention Training (AT), Working Memory Training (WMT), inhibitory control, and transfer effects such as fluid intelligence and behavior control. Another study, a Meta-analysis pointed toward the positive effect of cognitive training on preschool children's executive functions (EFs) in the age range of 3 to 6 years, without any risk (Nicoletta et al ., 2019). There are some findings documented from a similar study conducted by Nezla et al. in 2017 which reported significant ADHD core symptom improvements with multimodal treatment at a 6-month follow-up, and those results were better compared to those with single medication treatment (Nezla et al ., 2017). Rehabilitation programs for brain injuries (Sohlberg, M. M., & Mateer, C. A. (1987)) or Alzheimer (Gates, N. J., & Sachdev, P. (2014)) have also had success in either mild injuries or early onsets. Early training or differential experience (Rosenzweig 1996 ) can influence health, wellness, brain development, or successful aging. For children, a large body of prior studies is dedicated to improving learning struggles and cognitive deficits. The mode of delivery of such programs is online. The impact assessment and reception of online digital CT programs, in the scientific literature, has been mixed. The FDA’s recent approval of a CT game to treat ADHD might be an acknowledgment of the potential of a certain of these. Any article on cognitive enhancement cannot be complete without discussing on the ethics of cognitive enhancement. Much of the concerns raised on ethics looks at pharmacological interventions and advocates for increased discussion on proper public policy or regulation (Bostrom, N., Sandberg, A 2009). Brighter Minds The study presented in this article is related to examining a multimodal cognitive training (MCT) program, the Brighter Minds (BM) (Madhavi Bongarala, 2019 ), through a direct classroom mode of delivery to children with the help of trained school teachers. The study by Joseph et al. has documented that, use of recommended practices for developing skills can help in positive improvements in program outcomes in such school-based programs. (Joseph et al ., 2011). Earlier school-based study program on universal social and emotional learning (SEL) involving 270,034 kindergartens through high school students concluded that school teaching staff can successfully conduct SEL programs. The BM is an 8-week program, in-classroom sessions, with two consecutive sessions in the first week and seven follow up sessions, each session lasting between 3 hours to 3.5 hours. It requires a total of at least thirty hours of training, and provides a learning environment based on joy, positivity, and love. It incorporates a series of interactive proprietary tools and activities such as progressive muscle relaxation, breathing exercises, brain exercises and music entrainment (Siddaramappa, 2019 ), aerobic exercise, eye exercises and others ( Jacbobson 1987, Zaccaro 2018, Rogge 2017). For children from rural or from lower socio-economic strata, the program is offered without any charge by mobilization of funds through corporate social responsibility (CSR) partners and donors. Through the BM, many young children are encouraged and trained to improve cognitive skills that help them meet future challenges as children and adults in family, work and community. We look at the cause-effect relationship in the framework of focused BPS by varying one or more parameters and observing the effect on traits and short-term behaviors. Traits in a child may include focus, memory, intuitive thinking, self-confidence, comprehension, and others. We assume that all others influencing the effect such as social or biological parameters are constant during this study as all the participating children are from a similar background. In (Rueda, M. et al. 2005 ), the authors had studied the effect of executive attention training on temperament through children’s behavior questionnaires (CBQ) (Rothbart, M. et al. 1994 ). We explore the hypothesis that improvement in a child's traits by MCT can bring about changes to behavior (Fig. 1). Methods Study Design (Controlled before and after study) In Fig. 2, we show the set of inclusion criteria for selection of the intervention and control group based on a randomized design. Both the groups were tested before and after the BM training program. The terms intervention and case are used interchangeably during this article. Sample Size The sample size is selected based on some assumptions from prior literature (Madhavi Bongarala, 2019 ) that at least 33% will show positive improvement in the sample for the majority of the cognitive abilities. In considering this, a sample size of 168 students (84 intervention and 84 controls) were chosen for the study. Sample size was increased by 10% to accommodate sample loss due to various reasons and the final sample size included for the study were 186 (93 intervention and 93 controls) students. Inclusion and Exclusion Criteria Children, between the age of 10–15 years of age, who had signed up for an ongoing BM program were included for the study. Those children who might not be regular or had a risk of discontinuing the program mid-way were excluded. Sampling Technique Three locations (Gangtok, Mahabubnagar, Bangalore) were selected with purposive sampling from different sites of India, where children were enrolled into the training program. Within each of the study locations, multistage random sampling technique was used to select the children for the study Study Tools We have used several tools to assess the impact and these are listed below: A) Similar to CBQ (Rothbart, M. et al. 1994 ), structured interviews were used to record parental/ caregivers’ observations and to collect socio-demographic details (For Parents/ Caregivers – Both pre- and post-intervention) B) The psychometric tests used in this study are usually recognized to screen the self-esteem, memory, abstract, verbal, spatial, and mathematical reasoning ability (Phillip et al. , 2014; Madhavi et al. , 2019; van der Donk, 2016 ; Karch et al., 2013 ). C) Electroencephalogram (EEG) tests were conducted among 26 randomly selected children from the sample of control and intervention. To account for dropouts, about 10%, the final numbers enrolled were 28. Self-esteem might refer to an individual’s subjective evaluation of his or her worth as a person. It might not refelect on that child’s innate skills, talents or abilities or even how the child might be perceived by others. In Fig. 2, the sampling frame and workflow is illustrated. Statistical Analysis and Unsupervised Learning Given the two groups, the control and intervention, the paired t-test on the pre and post 8-week data is used to test the null hypothesis that the two groups have identical average (expected) values (or the difference between the expectation is zero) for the Psychometric and interview data. The p values for the statistical analysis provides an evidence to reject or not reject the above null hypothesis, and the threshold is fixed at 0.05. The Factor Analysis of Mixed Data (FAMD), like the Principal Component Analysis (PCA), is usually used to analyze a dataset having mixed variables that are both qualitative and quantitative. The qualitative variables, obtained from both the Psychometric tests and the interview results, are converted to numerical values by encoding them. The corresponding loadings and scores, in the reduced dimension, is further analysed for insights. Although the data is labelled as control and intervention, in order to study the effect of the exposure of the control to the training program or study the effect of location, we assumed that the data is unlabeled and analyzed the data using unsupervised learning methods. Electroencephalogram Studies Electroencephalogram (EEG) was selected in this study for the ease of administering in remote locations where laboratory access is limited and for the high temporal resolution. There is a wider acceptability of EEG to study improvements in cognitive functions (Wakeman & Henson, 2015 ; Heinrich et al. , 2018; Marthe et al. , 2015). Twenty-eight children were chosen at random from the original pool of children (with equidistribution of control and intervention) from the Mahbubnagar district in Southern India, accounting for 10% drop rates. 18 female and 10 male participants were picked up after excluding the following factors: presence of any neurological disease, hearing disorders or prior musical training or consumption of any psychoactive drugs. The average age for this sub-study was about 14.18 years with a standard deviation of about 1.34 years. The facilitators and the students were briefed about the purpose of the study and the experimental design in their local languages. These children were then randomly assigned to the control or intervention group. Baseline EEG readings were collected with eyes closed using a five-electrode research-grade EEG device (Krigolson O E 2017) that uses dry electrodes. The electrodes are placed at the following locations: AF7, AF8, AF9, TP9 and TP10 as per the 10–20 International Standards, and with the electrode Fpz marked as the reference electrode. The data sampling rate for the acquisition was fixed at 256 Hz ( > > Nyquist frequency) for all the subjects. The participants were seated on a chair comfortably, in a noise-free room, with no external stimulus. The room where the recordings were taken were kept ambient noise free but were not sound-proof, and so any external disturbances or external auditory inputs were manually edited out or the readings entirely discarded. The baseline EEG data was recorded, with eyes closed, prior to the BM program and also post the training program. The studies were administered directly by the lead researchers who were blind to the control and intervention group list. Pre-intervention EEG readings were carried out in December 2018, and the Post-intervention EEG studies were carried out with both the control and intervention groups in March 2019. The pre-processing of the entire dataset was done using MATLAB® (MathWorks®, Natick, MA, United States) with EEGLab Toolbox (Delorme A 2004 ), and the pre-processing protocol was adopted from the HAPPE pipeline (Gabard-Durnam LJ 2018) with minor changes. The only difference with the HAPPE pipeline was that if there was any noise while collecting the data, rather than interpolating the signal or dropping out the channels, the entire dataset was discarded. We filter the data with a high pass filter from 1 Hz, which removes the non-stationary signal drift across the recordings. As mentioned in (Maess et al. 2016 ), under noisy conditions, as in our case of measurements obtained from experiments outside an electromagnetically shielded lab, the usage of a high-pass filter is the better option than detrending or baseline correction. Any noise or artefacts in the channels were checked for by using wavelet-enhanced ICA (wICA), as mentioned in (Gabard-Durnam LJ 2018). Results Participants Profile The distribution of intervention and control cases in the three different study locations and the sociodemographic profile of the sample is as given in Table 1. Table 1 Sociodemographic Profile of the Study Samples Intervention (%) Controls (%) Total Distribution of the study participants Mahabubnagar 62 (75.6) 36 (73.5) 98 (74.8) Gangtok 16 (19.5) 13 (26.5) 29 (22) Bengaluru 04 (4.9) 00 (0) 04 (3.1) Gender Male 30 (36.6) 07 (14.9) 37 (28.2) Female 52 (63.4) 42 (85.7) 94 (71.8) Schooling details Preschool (Playschool/LKG UKG) 01 (1.2) 00 (0) 01 (0.8) Primary Education (1st to 5th grade) 01 (1.2) 13 (26.5) 14 (10.7) Middle school/Upper Primary school (6th to 8th grade) 43 (52.4) 25 (51.0) 68 (51.9) Secondary School (9th to 10th grade) 37 (45.1) 11 (22.4) 48 (36.6) Residence Rural 63 (76.8) 36 (73.5) 99 (75.6) Urban 19 (23.2) 13 (26.5) 32 (24.4) Child lives with With parents 49 (59.8) 13 (26.5) 62 (47.3) With grandparents 04 (4.9) 00 (0) 04 (3.1) With relatives 01 (1.2) 00 (0) 01 (0.8) With caregivers (Hostel) 28 (34.1) 36 (73.5) 64 (48.9) Total 82 (100) 49 (100) 131 (100) The age of the children, who participated in the study, ranged between 10 and 15 years, with the mean age of children being 13.08 years. About 71.75% of children were female, 75.6% were residing in rural areas. 47.32% of children were residing with their parents (59.75% of intervention and 26.53% of controls); 48.85% of children were residing in hostels (34.14% of intervention and 73.46 % of controls). 51.91% of children were in upper primary school; 36.64% were in secondary school; 10.69% of children were studying in primary school. Parent/ Caregiver Interview Results The statistical analysis of the interview results of the parents and caregivers are listed in Table 2 for both the groups and also the significance values. Table 2 Summary of Parental Reports - Comparison of Intervention and Controls (Pre and Post) Intervention Controls F eedback Pre-test N (%) Post-test N (%) Sig. Pre-test N (%) Post-test N (%) Sig Focus Good 34 (41.5) 50 (61.0) 0.043 19(38.8) 27(55.1) 0.228 Average 34 (41.5) 22 (26.8) 21(42.9) 17(34.7) Poor 14 (17.1) 10 (12.2) 9(18.4) 5(10.2) Total 82 82 49 49 Observation Good 41 (50.6) 46 (605) 0.279 20(41.7) 24(51.1 0.643 Average 31 (38.3) 20 (20.6) 20(41.7) 17(36.2) Poor 9 (11.1) 10 (13.2) 8(06.7) 6(12.8) Total 81 76 48 47 Memory Good 38 (46.3) 50 (60.5) 0.168 19(38.8) 24(51.1) 0.351 Average 28 (34.1) 21 (25.6) 22(44.9) 19(40.4) Poor 16 (19.5) 11 (13.4) 8(16.3) 04(8.5) Total 82 82 49 47 Self-confidence Good 33 (40.2) 49 (59.8) 0.43 23(46.9) 28(58.3) 0.265 Average 38 (46.3) 25 (30.5) 22(44.9) 14(29.2) Poor 11 (13.4) 8 (9.8) 4.(8.2) 6(12.5) Total 82 82 49 44 Participation in actives Often 35 (43.2) 48 (59.3) 0.120 19.(38.8) 24(50.0) 0.404 Some time 28 (34.6) 21 (25.9) 21(42.9) 19(39.6) Rarely 18 (22.2) 12 (14.8) 9(18.4) 5(10.4) Total 81 81 49 48 Empathy Frequently 36 (44.4) 54 (66.7) 0.014 18(37.5) 27(56.3) 0.131 Moderately 23 (28.4) 16 (19.8) 18(37.5) 15(31.3) Rarely 22 (27.2) 11(13.6) 12(25.0) 6(12.5) Total 81 81 48 48 Understanding others Good 34 (41.5) 41(52.6) 0.110 16(34.8) 13(27.7) 0.066 Average 30 (36.6) 29(37.2) 16(34.8) 27(57.4) Poor 18 (22.0) 8(10.3) 14(30.4) 7(14.9) Total 82 78 46 47 I ntuition Good 29 (35.8) 47 (57.3 0.019 15(30.6) 18(37.5) 0.614 Average 34 (42) 25 (30.5) 26(53.1) 25(52.1) Poor 18 (22.2) 10 (12.2 8(16.3) 5(10.4) Total 71 82 49 48 Comprehension Good 30 (37.0) 42 (52.5) 0.020 16(34) 21(42.9) 0.589 Average 30 (37.0) 30 (37.5) 26(55.3) 22(44.9) Poor 21 (25.9) 8 (10.0) 5(10.6) 6(12.2) Total 81 80 47 49 Abstract Concept Understanding Good 31(39.2) 55(67.1) 0.002 17(34.7) 23(46.9) 0.082 Average 29(36.2) 17(20.7) 20(40.8) 22(44.9) Poor 19(24.1) 10(12.2) 12(24.5) 4(8.2) Total 79 89 49 49 Among the intervention group, statistically significant improvements were seen in child’s focus (P < 0.05), empathy (P < 0.05), intuition (P < 0.05), comprehension (P < 0.05) and child’s abstract concept understanding (P < 0.01). Psychometric Test Results Table 3 Statistical Significance Test for comparing mean values of Psychometric test results between Intervention and Control Intervention (n = 82) Control (n = 49) Base line End line Sig. (P < 0.01) Base line End line Sig. (P < 0.01) Raven’s SPM 30.69 31.95 0.48 23.43 27.78 0.028 MMSE 25.17 27.89 0.000 26.39 28.16 0.019 Memory Recall (Nonverbal) 84.89 116.31 0.000 87.18 109.10 0.000 Susan Harter’s Test 53.80 56.67 0.50 50.20 50.51 0.94 Study of psychometric test results shows that statistically significant improvements were seen in tests of MMSE (P < 0.01), and Memory profile (P < 0.01) for the intervention group. However, statistically significant improvements were also seen in the control group for Memory Profile (P < 0.01) (Table 3). EEG Test Results In Fig. 4, we show the power spectral density (PSD) plot of the signal collected from two electrodes located in the pre-frontal lobe (locations AF7 in purple and AF8 in green) for a child who underwent the BM program. This participant, a twelve-year-old female child, was randomly chosen from among all the children who had participated in the program. We do not normalize the power spectrum by spectral flattening or baseline normalization as we would like to compare the rate of the ‘1/f’ drop for control and intervention. Unsupervised Learning Results In this section, we learn and draw insights from the entire data by adopting some recent trends in unsupervised learning approaches. One such approach is the dimensionality reduction algorithm such as Principal Component Analysis (PCA). However, the data comprised of a mixture of both qualitative and quantitative variables. In Fig. 5 (below), we show the scatter plot of the components after Factor analysis (Escofier Brigitte & Pagès Jérôme (2008)) of the data which is a mixture of quantitative and qualitative. FAMD was chosen to include also the categorical variables along with the continuous variables. The columns that specified the location of the training program was removed prior to factorization, and only used for visualization and color labelling. Discussion In recent years, literature shows a growing interest among researchers in studies related to early childhood development and the role of brain and cognition enhancement during the formative years of childhood. The objective of this article is aimed at exploring the hypothesis that multi-modal cognitive training can influence the inherent traits and bring a change in a child's external behavior. It is one of the first of its kind that used a controlled before and after study design to assess effectiveness of MCT among children under general settings, and evaluate their implications. The tests that are canvassed in this study were chosen to cover most of the spheres of cognitive abilities, because of its reliability and the validity tested in the previous research conducted (Madhavi and Jayanna, 2019). The parent and caregiver interviews in our study reported improvements in cognitive traits such as the child’s focus, empathy, memory, intuition, comprehension and understanding of abstract concepts, and improvements in behavior traits such as self-confidence, empathy, and participation. These findings are comparable to the results of the study conducted recently in ( Madhavi & Jayanna, 2019), who also reported within the educational settings. The Psychometric Test results revealed statistically significant improvements in the MMSE (P < 0.01) for the intervention group. However, for both the control and the intervention group, statistically significant improvements were seen for Memory (P < 0.01). It had been reported (Nezla et al ., 2017.) that self-worth and self-competence in different settings like scholastics, social acceptance, athletic competence, physical appearance, and behavioral conduct improved with multimodal brain training programs. From our results, it could not be concluded if the BM program brought any significant difference in Memory, Raven’s SPM or Susan Harter’s Test to the intervention as against control participants. A similar conclusion can be drawn from the Violin Plots in Fig. 3, where we see significant improvements in MMSE but not the other tests. One possible explanation in the case of Susan Harter’s test is that it was observed that children had in general very low self-esteem scores. New findings on this subject believes that self-esteem is relatively stable trait and it is similar to some basic personality characteristics. Another reason for the difference could be attributed to the lower number of samples of Control than Intervention participants, and hence seeming to appear from a different statistical distribution. This might also be explained by a new theory (Jaeggi, S. M., 2020) that was put forth suggesting that the outcomes may be averaged across all individuals who received the intervention, and benefit experienced by a few might be averaged out. Another possibility is that the program does not improve fluid intelligence due to the shorter time span between training intervals (< 7days) (Wang, Z. et al. 2014 ). The SPM results might be pointing out to the fact that while some abilities are enhanced after the training program, a few others might have impaired or diminished (Colzato, L. S. et al. 2020 ). Figure 4a is the PSD calculated on the baseline EEG signals for the control, and Fig. 4b was the PSD calculated from the processed EEG signal for the intervention after the eight weeks training program. It was reported that in the waking state with eyes closed, the frequency band below 20Hz scales as 1/f, and with possible peak around 20Hz, while in the slow-wave sleep state, the ‘1/f’ scaling is not visible (Bedard et al. 2006 ). We observed a similar trend as well, except this difference was observed between the control and the intervention group. In the control group, the ‘1/f’ scaling is visible (see Fig. 4a red line for local slope) but in the case of the intervention, the drop is much steeper than for the control (see Fig. 4b orange line for local slope). Our test results of EEG on a selected sub-sample of children, showed that the spectral signatures of the EEG in the intervention group were quite similar to those with spectra from EEG recorded during a relaxed or slow sleep state. We observed that, in general, the spectrum for the intervention group exhibited a drop in the higher frequencies, especially above 12 Hz (see Fig. 4b) frequencies. This raises the interesting reflection if the intervention program might have led to transfer effect on other skills like relaxation and a permanent change in their inner traits in comparison to the control group. It is known that Power-Law scaling governs the brain’s surface electric potentials (Miller et al. 2009 , Bedard et al. 2006 ) and is also an intrinsic property of complex dynamic systems (Marković, D. and Gros, C. 2014 , Meisel, C., Bailey, K., Achermann, P. et al. , 2017). During FAMD, for visualization, we can see in Fig. 5, that the ellipses separate the two data coming from the two locations Mahabubnagar and Gangtok very distinctly. The location clustering could be an indicator to a hidden latent variable-the effect of the training facilitator (here teachers) on the training program. When we removed the influence of the locations in the analysis, for the data from the Gangtok site, there is a clear grouping happening between the Intervention and the Control groups (see Fig. 6) after the Factor Analysis. However, for the Mahabubnagar location (see Fig. 7), the distinction between the Control and intervention data is not distinct. This could be explained by the fact that the control group children from the Mahabubnagar program were living in hostels and the program leaked through the intervention group. Limitations of the study The study has a few limitations. Identification and recruitment of children who are naturally seeking admission into the Brighter Minds program was a challenge in study sites outside of Mahabubnagar. Fewer children were enrolling into the program in the learning centers on these sites. Even though we had identified and recruited 186 participants (93 intervention and 93 controls) in the baseline study, only 131 participants (82 intervention and 49 control cases) were available for end line assessment. The remaining cases (about 30%) were lost from the sample due to exposure of controls to intervention programs and drop out because of illness. This 30% loss was much higher than our anticipated loss of 10%. Many children, from the schools in Mahabubnagar district, were residing in hostels, and there are chances of exposure of the control participants to the intervention (see Fig. 7 and Fig. 8). It is also likely that some of the facilitating teachers from these rural schools do understand the importance of proving an environment that can improve the children’s confidence and self-esteem. The logistical complexity of conducting the controlled studies in community settings such as children's hostels will need to be taken into account in future studies. This also violated our initial assumption that the biological and social conditions during the study will not change or influence the study. It was also not possible to evaluate clearly what cognitive skill was impaired after the training program. For the EEG analysis, in order to simplify the recordings for children, we restricted the number of electrode sensors to 5 and were dry electrodes. Dry electrodes can bring in additional noise due to a temporary loss in contact with the skin. This was partly mitigated by using gel but we lost the signal from the dry electrodes and had to redo the experiments. As the number of electrodes are only a few, the recordings might not be precise for source localization using methods such as ICA. The recordings are done in a relatively noise-free room, but it is not shielded completely from outside noise. Advanced EEG analysis in a laboratory-controlled settings and functional MRI in future studies have potential to shed light on trait changes in the children that undergo cognitive and behavioural changes as a result of cognitive training. Conclusions Brighter Minds' multimodal cognitive training program has a positive effect on self–worth and self-competence as well as cognitive skills such as comprehension, observation, intuition, and focus. Ability of children for abstract thinking, verbal and spatial abilities, and mathematical reasoning when improved, will in turn benefit achievements and comprehensive abilities of children. The program duration and feasibility to implement in diverse settings including the schools, shows that the benefits of this program potentially outweigh the efforts. Early childhood education requires a paradigm shift as emphasized by India National Education Policy (2020) to advance human potential and development, and the present study provides new evidence and direction in this regard. Declarations Acknowledgments The authors would like to thank all the parents and schoolteachers who provided their consent and helped in participation of children in the evaluation exercises. The authors would like to thank the Research team at Institute of Health Management Research, Bengaluru (IIHMR-B), Dr. S R Chandrasekhar Institute of Speech and Hearing, Center for Integrative Health and Wellbeing (CIHW) for supporting intervention and evaluation exercises of this research project, and Ashish Sinha (BITS Pilani, Goa) for the help with the EEG data processing. Funding The infrastructure and logistics required for the study was funded by Cognitive Skills Pvt Ltd. The donor did not have any role in the study design, study implementation, or data collection and analysis. All the authors involved in this study received funding from independent sources. Competing interests PP and KJ are unpaid research advisors at Brighter Minds. There are no other competing interests to declare for all other authors. Authors’ contributions MR: Study designing, Methodology, Validation, Investigation, Report Writing AJ: Planning of psychometric tests, execution of psychometric tests, report writing PP: EEG Study Design, EEG Data Acquisition, EEG Formal Analysis, Statistical Analysis, Investigation, Curation, Data Visualization, Report Writing GCK: Statistical analysis, Report writing UM: Field planning, monitoring and supervision of data collection activities KJ: Report Review, Funding acquisition, Project conceptualization, Supervision Ethics Approval Institutional ethics committee of IIHMR University reviewed the proposal, research tools, and consent forms and provided ethical approval. Before reaching any intervention, it was ensured that all approvals from the authorities were in place. All ethical procedures were followed in guidance from the Institutional Ethical Review Board (IERB) of IIHMR University, Jaipur, India. Consent to participate Written informed consent, in local language was obtained from parents/caregivers. They were explained about the purpose of the study and that the confidential information collected will be masked. Data availability The datasets generated during and/or analysed during the current study are available from the Mendeley repository here: https://data.mendeley.com/datasets/szf5gz82jp/1 References [1] Bandla, S., Mandadi, G. D., & Bhogaraju, A. (2017). Specific Learning Disabilities and Psychiatric Comorbidities in School Children in South India. Indian journal of psychological medicine , 39 (1), 76–82. https://doi.org/10.4103/0253-7176.198950 [2] Benning T. Limitations of the biopsychosocial model in psychiatry. Adv Med Educ Pract. 2015;6:347-352 [3] Bedard, Claude & Kroeger, Helmut & Destexhe, Alain. (2006). Does the 1/f Frequency Scaling of Brain Signals Reflect Self-Organized Critical States?. Physical Review Letters. 97. 10.1103/PhysRevLett.97.118102. 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Adult neurogenesis and neuroplasticity. Restorat Neurol Neurosci 2006; 24: 9–15. [55] Titz, C., and Karbach, J. Working memory and executive functions: effects of training on academic achievement. Psychol. Res 2014. 78, 852–868. doi: 10.1007/s00426-013-0537-1 [56] Teicher MH, Andersen SL, Polcari A, Anderson CM, Navalta CP, Kim DM. The neurobiological consequences of early stress and childhood maltreatment. Neurosci Biobehav Rev. 2003;27(1-2):33-44. doi:10.1016/s0149-7634(03)00007-1 [57] Tomporowski, P. D., McCullick, B., Pendleton, D. M., & Pesce, C. (2015). Exercise and children's cognition: the role of exercise characteristics and a place for metacognition. Journal of Sport and Health Science , 4 (1), 47-55. [58] Van der Donk, M. L. A. (2016). Cognitive training for children with ADHD: Individual differences in training and transfer gains . Doctoral dissertation). Retrieved from: http://hdl. handle. net/11245/1.534724. [59] Wakeman, D. G., & Henson, R. N. (2015). A multi-subject, multi-modal human neuroimaging dataset. Scientific data , 2 , 150001. [60] Yeniad, N., Malda, M., Mesman, J., van Ijzendoorn, M. H., and Pieper. Shifting ability predicts math and reading performance in children: a meta-analytical study. Learn. Individ. Differ 2013, 23, 1–9. doi: 10.1016/j.lindif.2012.10.00 [61] Wang, Z., Zhou, R., Shah, R. (2014) Spaced cognitive training promotes training transfer, Front. Hum. Neurosci, 8, 217. doi: 10.3389/fnhum.2014.00217 [62] Zaccaro A, Piarulli A, Laurino M, Garbella E, Menicucci D, Neri B and Gemignani A (2018) How Breath-Control Can Change Your Life: A Systematic Review on Psycho-Physiological Correlates of Slow Breathing. Front. Hum. Neurosci. 12:353. doi: 10.3389/fnhum.2018.00353 [63] Zwilling, C. E., Daugherty, A. M., Hillman, C. H., Kramer, A. F., Cohen, N. J., & Barbey, A. K. (2019). Enhanced decision-making through multimodal training. NPJ science of learning , 4 (1), 1-10. Additional Declarations Competing interest reported. PP and KJ are unpaid research advisors at Brighter Minds. There are no other competing interests to declare for all other authors. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-468572","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":24181363,"identity":"56dbdec1-5e84-4b9b-aee4-fbe97d41a29b","order_by":0,"name":"Manjunatha R","email":"","orcid":"","institution":"IIHMR University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Manjunatha","middleName":"","lastName":"R","suffix":""},{"id":24181364,"identity":"009043ff-60e0-4b9a-84a0-c4d75c4e8b54","order_by":1,"name":"Praveen Pankajakshan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHADNgYGiQoJkrWcIVkLYxsR6sylDz+T+FBxJ49/dlviB8t5FnL87A2Mjyt+4dZi2ZdmJjnjzLNiiTvHDktIbpMwluw5wGx4tg+3FoMzDMbGvG2HExtupDeAtCRuuJHAJtnYg08L+2fjv/8OJ86/kd78Q3KORP3++w8IaeExfMzYcBhoeNoxCckGiQQDCQY2yYYfePzSw1P4sOfY4WLDG2lpFhLHJAxnnElsNmxswK3FnId9w4EfNYfz5G6kGd+WqKmT528/fPBhwx88DoPSCSCCGRKRjA14IwhFC+MHuDgeW0bBKBgFo2DEAQD7P1VT69PUAgAAAABJRU5ErkJggg==","orcid":"","institution":"Amrita Vishwa Vidyapeetham University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Praveen","middleName":"","lastName":"Pankajakshan","suffix":""},{"id":24181365,"identity":"76d0bf9e-dbe4-4864-8b44-dbd629394eb5","order_by":2,"name":"Alphonsa Joseph","email":"","orcid":"","institution":"Dr. S.R. Chandrasekhar Institute of Speech and Hearing","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alphonsa","middleName":"","lastName":"Joseph","suffix":""},{"id":24181366,"identity":"3e3329ea-ce80-4dab-a534-fd5fee75183a","order_by":3,"name":"Gyan Kashyap","email":"","orcid":"","institution":"IIHMR University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gyan","middleName":"","lastName":"Kashyap","suffix":""},{"id":24181367,"identity":"380fcc2e-e569-41ea-bec4-e5c629e8384a","order_by":4,"name":"Usha Manjunath","email":"","orcid":"","institution":"IIHMR University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Usha","middleName":"","lastName":"Manjunath","suffix":""},{"id":24181368,"identity":"1b5550f7-1ab7-4fde-952d-6cde89e4f388","order_by":5,"name":"Krishnamurthy Jayanna","email":"","orcid":"","institution":"M S Ramaiah University of Applied Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Krishnamurthy","middleName":"","lastName":"Jayanna","suffix":""}],"badges":[],"createdAt":"2021-04-27 11:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-468572/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-468572/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":8633146,"identity":"014648d8-27e6-4f6a-a046-54bc3528a10b","added_by":"auto","created_at":"2021-04-30 12:00:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29588,"visible":true,"origin":"","legend":"Multimodal Cognitive Training can influence traits such as focus, observation and empathy, and have transfer effects on behavior.","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-468572/v1/8c0471ff3d668aad9a8508e2.png"},{"id":8633344,"identity":"444cd367-5912-4046-893b-315c262d96f0","added_by":"auto","created_at":"2021-04-30 12:06:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":273717,"visible":true,"origin":"","legend":"Sampling Frame and Workflow","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-468572/v1/5163e861a89c01761f72e4a5.png"},{"id":8633153,"identity":"d0b403e3-a231-49f8-aaeb-c02f6cdb3a03","added_by":"auto","created_at":"2021-04-30 12:00:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95587,"visible":true,"origin":"","legend":"Violin Plots for the two participant groups, Control and Intervention (Case) for the Psychometric Tests of (a) Memory Recall (b) SPM, (c) Susan Harter’s (d) MMSE. Violet and Green represent Pre-CT, and Red and Blue represent Post-CT. Error bars indicate 95% confidence interval.","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-468572/v1/cdc480669f3871d834e2009c.png"},{"id":8633310,"identity":"95291687-d576-4df9-b8ea-7919bc310c55","added_by":"auto","created_at":"2021-04-30 12:03:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82657,"visible":true,"origin":"","legend":"The Power Spectral Density (PSD) calculated on the AF7 (purple color) and AF8 (green color) EEG signals subject (a) Control; and (b) Intervention. Red and Orange lines indicate the local slopes for the PSD below 20Hz for the Control and Intervention respectively.","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-468572/v1/d343cfd3d28dcb9e6a197a8c.png"},{"id":8633313,"identity":"78986c14-8d65-45ac-a7b9-71c834f01aad","added_by":"auto","created_at":"2021-04-30 12:03:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":416751,"visible":true,"origin":"","legend":"(above) Chord Diagram of the entire dataset with Control and Intervention Grouping based on the Dice Distance; (below) Scatter Plot after Factor Analysis on the Mixed Data (Categorical and Continuous) and colors representing the location of the training program (Bengaluru, Gangtok, Mahabubnagar).","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-468572/v1/9cd3b2c6bf83347b3b7af09e.png"},{"id":8633312,"identity":"47d19dbe-e281-4d5a-b926-2574c73b3d0f","added_by":"auto","created_at":"2021-04-30 12:03:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":26376,"visible":true,"origin":"","legend":"Scatter Plot after Factor Analysis on the Mixed Data (Categorical and Continuous) on location Gangtok. The purple color represents the Control group while the Cyan color represents the Intervention Group.","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-468572/v1/b0b83d72b3ef2002bb4e0990.png"},{"id":8633343,"identity":"c21051cf-8e7d-44e4-9011-590c7649e00c","added_by":"auto","created_at":"2021-04-30 12:06:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":35664,"visible":true,"origin":"","legend":"Scatter Plot after Factor Analysis on the Mixed Data (Categorical and Continuous) and on the dataset collected from the location Mahabubnagar. The purple color represents the Intervention group while the orange color represents the Intervention Group.","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-468572/v1/80b6de6015f010f0eabffb3e.png"},{"id":8633345,"identity":"5bca6eb4-bf6b-422c-859e-e02b27b1176c","added_by":"auto","created_at":"2021-04-30 12:06:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":86789,"visible":true,"origin":"","legend":"Unsupervised Clustering of the total dataset consisting of the categorical variables from the Psychomteric Tests and the Parent/Caregiver survey results, using dice metric. The Case and the Intervention clearly cluster as a separate group. The Case group form two major clusters based on the two locations: Mahabubnagar and Gangtok. It is likely that the anomalies in the Control appearing among the intervention group might be due to the exposure of the program to the control participants in hostels.","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-468572/v1/342c574a786e24b4a411d880.png"},{"id":13689539,"identity":"5317ba92-3064-462d-b9b3-c444731d544b","added_by":"auto","created_at":"2021-09-17 12:29:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1487378,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-468572/v1/83823903-1ba5-463f-9432-fda67a4ad73b.pdf"}],"financialInterests":"Competing interest reported. PP and KJ are unpaid research advisors at Brighter Minds. There are no other competing interests to declare for all other authors.","formattedTitle":"Impact of Multimodal Cognitive Training on Cognitive Traits of Children: A Multicentric Interventional Study","fulltext":[{"header":"Introduction","content":" \u003cp\u003eIt is a long-held notion that learnings and impressions that are experienced at an early age, as a child, can have impact on the neuroanatomy and several neural structures for a long-term (Teicher et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). There are several evidences that link genetic, environment and life-style factors to the development or lack of development of the structure and functional aspects of the brain. The cognitive flexibility of a child or the ability to adapt the thoughts and behavior in response to changes in goals or the environment in life (Chevalier et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) could be attributed to these aspects. With changes in the environmental task demands, the working memory, attention, and response selection also changes accordingly. The degree of the change determines the degree of flexibility. Among children, flexibility develops rapidly during the preschool years and continues to improve across adolescence and young adulthood (Yeniad et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Titz and Karbach, 2014). Such cognitive skills, abilities (Hinshaw, 1994) and responses that are developed due to either or both of intrinsic and extrinsic dynamics in the early part of life are bound to prevail as traits throughout the lifespan. However, as the age advances, the cognitive ability to change in response to new extrinsic experiences reduces. The interplay of genes and in-utero experiences might be responsible for some children being born with hypersensitive behavior facilitation (Gray 1987) or an underactive behavioral inhibition system (Scarpal \u0026amp; Raine, 2000). It is thus necessary to develop a balanced approach to emotional, social, cognitive and language development for preparing children to success in school, and later on as adults in family, workplace and community (In Brief: The Science of Early Childhood Development, 2018).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eCognitive Training\u003c/h2\u003e \u003cp\u003eThe biopsychosocial (BPS) framework (Grinker RR Sr. 1964, Engel \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), was the earliest to provide an integrative, comprehensive model to human development, health, and functioning. It looks at the interconnection between biology, psychology, and socio-environmental factors, to understand how these influence health, disease and wellness. It has now established itself as the status quo model for contemporary medicine. Engel declared that this model was developed to overcome the limitations and to bring contrast to the biomedical model which was prevalent at that time. It is the claim that the BPS model itself takes its roots from the general systems theory (GST). Critics of the BPS (Ghaemi \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Benning \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e and Bolton \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) argue that the BPS model is very vague, eclectic and unfaithful to GST, and is not scientific in its approach. Another contention, related to the above subject, is that the GST itself might not be a right framework to address the complexity of the mind (McLaren 1998). These theories and models must be understood in the context of trying to understand or identify the root cause of observed effects such as behavior, disease or health. We acknowledge that it is difficult albeit sometimes impossible to determine the exact causal basis to some of these effects (Karunamuni 2020); because the cause-effect relationship and pathways might be multivariate with latent variables, and highly nonlinear pathways. There are alternative models proposed recently (Ghaemi SN 2009), which focus on the humanistic element to medical treatment, but we focus our attention on the psychological component of the BPS. The idea is to seek a psychological foundation for a behavior or behaviors.\u003c/p\u003e \u003cp\u003eCognitive Training (CT) aims at exploring the central idea that the neuronal structure and function can be changed by altering either the environment or experience or both. Some of the earliest results from literature (Douglas, V.I. et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1976\u003c/span\u003e) (Shaw, C., Lanius, R., \u0026amp; Doel, K.V., 1994) provide light along these directions. As an alternative to pharmaceutical interventions, parents are turning to CT for their children and adolescents to enhance cognitive abilities. Similarly, caregivers of elderly citizens are turning to CT for reducing the onset of age related dementia. Studies have reported the effects of CT on changes in cognition and behavior, across multiple disciplines: developmental psychology, social psychology, clinical psychology, educational psychology, and cognitive psychology (Jedlicka, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Numerous techniques conceptualize how children's cognitive ability can be enhanced through CT (Jedlicka, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Madhavi \u0026amp; Jayanna, 2019). In Rabipour, Sheida \u0026amp; Raz, Amir. 2012, the authors review published literature on CT for Attention Training (AT), Working Memory Training (WMT), inhibitory control, and transfer effects such as fluid intelligence and behavior control. Another study, a Meta-analysis pointed toward the positive effect of cognitive training on preschool children's executive functions (EFs) in the age range of 3 to 6 years, without any risk (Nicoletta \u003cem\u003eet al\u003c/em\u003e., 2019). There are some findings documented from a similar study conducted by Nezla \u003cem\u003eet al.\u003c/em\u003e in 2017 which reported significant ADHD core symptom improvements with multimodal treatment at a 6-month follow-up, and those results were better compared to those with single medication treatment (Nezla \u003cem\u003eet al\u003c/em\u003e., 2017). Rehabilitation programs for brain injuries (Sohlberg, M. M., \u0026amp; Mateer, C. A. (1987)) or Alzheimer (Gates, N. J., \u0026amp; Sachdev, P. (2014)) have also had success in either mild injuries or early onsets. Early training or differential experience (Rosenzweig \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) can influence health, wellness, brain development, or successful aging.\u003c/p\u003e \u003cp\u003eFor children, a large body of prior studies is dedicated to improving learning struggles and cognitive deficits. The mode of delivery of such programs is online. The impact assessment and reception of online digital CT programs, in the scientific literature, has been mixed. The FDA\u0026rsquo;s recent approval of a CT game to treat ADHD might be an acknowledgment of the potential of a certain of these. Any article on cognitive enhancement cannot be complete without discussing on the ethics of cognitive enhancement. Much of the concerns raised on ethics looks at pharmacological interventions and advocates for increased discussion on proper public policy or regulation (Bostrom, N., Sandberg, A 2009).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBrighter Minds\u003c/h2\u003e \u003cp\u003eThe study presented in this article is related to examining a multimodal cognitive training (MCT) program, the Brighter Minds (BM) (Madhavi Bongarala, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), through a direct classroom mode of delivery to children with the help of trained school teachers. The study by Joseph \u003cem\u003eet al.\u003c/em\u003e has documented that, use of recommended practices for developing skills can help in positive improvements in program outcomes in such school-based programs. (Joseph \u003cem\u003eet al\u003c/em\u003e., 2011). Earlier school-based study program on universal social and emotional learning (SEL) involving 270,034 kindergartens through high school students concluded that school teaching staff can successfully conduct SEL programs. The BM is an 8-week program, in-classroom sessions, with two consecutive sessions in the first week and seven follow up sessions, each session lasting between 3 hours to 3.5 hours. It requires a total of at least thirty hours of training, and provides a learning environment based on joy, positivity, and love. It incorporates a series of interactive proprietary tools and activities such as progressive muscle relaxation, breathing exercises, brain exercises and music entrainment (Siddaramappa, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), aerobic exercise, eye exercises and others ( Jacbobson 1987, Zaccaro 2018, Rogge 2017). For children from rural or from lower socio-economic strata, the program is offered without any charge by mobilization of funds through corporate social responsibility (CSR) partners and donors. Through the BM, many young children are encouraged and trained to improve cognitive skills that help them meet future challenges as children and adults in family, work and community. We look at the cause-effect relationship in the framework of focused BPS by varying one or more parameters and observing the effect on traits and short-term behaviors. Traits in a child may include focus, memory, intuitive thinking, self-confidence, comprehension, and others. We assume that all others influencing the effect such as social or biological parameters are constant during this study as all the participating children are from a similar background. In (Rueda, M. et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), the authors had studied the effect of executive attention training on temperament through children\u0026rsquo;s behavior questionnaires (CBQ) (Rothbart, M. et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). We explore the hypothesis that improvement in a child's traits by MCT can bring about changes to behavior (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy Design (Controlled before and after study)\u003c/h2\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;2, we show the set of inclusion criteria for selection of the intervention and control group based on a randomized design. Both the groups were tested before and after the BM training program. The terms intervention and case are used interchangeably during this article.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003ch2\u003eSample Size\u003c/h2\u003e\n\u003cp\u003eThe sample size is selected based on some assumptions from prior literature (Madhavi Bongarala, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) that at least 33% will show positive improvement in the sample for the majority of the cognitive abilities. In considering this, a sample size of 168 students (84 intervention and 84 controls) were chosen for the study. Sample size was increased by 10% to accommodate sample loss due to various reasons and the final sample size included for the study were 186 (93 intervention and 93 controls) students.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n\u003ch2\u003eInclusion and Exclusion Criteria\u003c/h2\u003e\n\u003cp\u003eChildren, between the age of 10\u0026ndash;15 years of age, who had signed up for an ongoing BM program were included for the study. Those children who might not be regular or had a risk of discontinuing the program mid-way were excluded.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n\u003ch2\u003eSampling Technique\u003c/h2\u003e\n\u003cp\u003eThree locations (Gangtok, Mahabubnagar, Bangalore) were selected with purposive sampling from different sites of India, where children were enrolled into the training program. Within each of the study locations, \u003cem\u003emultistage random sampling technique\u003c/em\u003e was used to select the children for the study\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy Tools\u003c/h2\u003e\n\u003cp\u003eWe have used several tools to assess the impact and these are listed below:\u003c/p\u003e\n\u003cp\u003eA) Similar to CBQ (Rothbart, M. et al. \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e), structured interviews were used to record parental/ caregivers\u0026rsquo; observations and to collect socio-demographic details (For Parents/ Caregivers \u0026ndash; Both pre- and post-intervention)\u003c/p\u003e\n\u003cp\u003eB) The psychometric tests used in this study are usually recognized to screen the self-esteem, memory, abstract, verbal, spatial, and mathematical reasoning ability (Phillip \u003cem\u003eet al.\u003c/em\u003e, 2014; Madhavi \u003cem\u003eet al.\u003c/em\u003e, 2019; van der Donk, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Karch et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eC) Electroencephalogram (EEG) tests were conducted among 26 randomly selected children from the sample of control and intervention. To account for dropouts, about 10%, the final numbers enrolled were 28.\u003c/p\u003e\n\u003cp\u003eSelf-esteem might refer to an individual\u0026rsquo;s subjective evaluation of his or her worth as a person. It might not refelect on that child\u0026rsquo;s innate skills, talents or abilities or even how the child might be perceived by others.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;2, the sampling frame and workflow is illustrated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis and Unsupervised Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the two groups, the control and intervention, the paired t-test on the pre and post 8-week data is used to test the null hypothesis that the two groups have identical average (expected) values (or the difference between the expectation is zero) for the Psychometric and interview data. The \u003cem\u003ep\u003c/em\u003e values for the statistical analysis provides an evidence to reject or not reject the above null hypothesis, and the threshold is fixed at 0.05.\u003c/p\u003e\n\u003cp\u003eThe Factor Analysis of Mixed Data (FAMD), like the Principal Component Analysis (PCA), is usually used to analyze a dataset having mixed variables that are both qualitative and quantitative. The qualitative variables, obtained from both the Psychometric tests and the interview results, are converted to numerical values by encoding them. The corresponding loadings and scores, in the reduced dimension, is further analysed for insights.\u003c/p\u003e\n\u003cp\u003eAlthough the data is labelled as control and intervention, in order to study the effect of the exposure of the control to the training program or study the effect of location, we assumed that the data is unlabeled and analyzed the data using unsupervised learning methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElectroencephalogram Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElectroencephalogram (EEG) was selected in this study for the ease of administering in remote locations where laboratory access is limited and for the high temporal resolution. There is a wider acceptability of EEG to study improvements in cognitive functions (Wakeman \u0026amp; Henson, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Heinrich \u003cem\u003eet al.\u003c/em\u003e, 2018; Marthe \u003cem\u003eet al.\u003c/em\u003e, 2015). Twenty-eight children were chosen at random from the original pool of children (with equidistribution of control and intervention) from the Mahbubnagar district in Southern India, accounting for 10% drop rates. 18 female and 10 male participants were picked up after excluding the following factors: presence of any neurological disease, hearing disorders or prior musical training or consumption of any psychoactive drugs. The average age for this sub-study was about 14.18 years with a standard deviation of about 1.34 years. The facilitators and the students were briefed about the purpose of the study and the experimental design in their local languages. These children were then randomly assigned to the control or intervention group. Baseline EEG readings were collected with eyes closed using a five-electrode research-grade EEG device (Krigolson O E 2017) that uses dry electrodes. The electrodes are placed at the following locations: AF7, AF8, AF9, TP9 and TP10 as per the 10\u0026ndash;20 International Standards, and with the electrode Fpz marked as the reference electrode. The data sampling rate for the acquisition was fixed at 256 Hz (\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;Nyquist frequency) for all the subjects. The participants were seated on a chair comfortably, in a noise-free room, with no external stimulus. The room where the recordings were taken were kept ambient noise free but were not sound-proof, and so any external disturbances or external auditory inputs were manually edited out or the readings entirely discarded. The baseline EEG data was recorded, with eyes closed, prior to the BM program and also post the training program. The studies were administered directly by the lead researchers who were blind to the control and intervention group list. Pre-intervention EEG readings were carried out in December 2018, and the Post-intervention EEG studies were carried out with both the control and intervention groups in March 2019. The pre-processing of the entire dataset was done using MATLAB\u0026reg; (MathWorks\u0026reg;, Natick, MA, United States) with EEGLab Toolbox (Delorme A \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e), and the pre-processing protocol was adopted from the HAPPE pipeline (Gabard-Durnam LJ 2018) with minor changes. The only difference with the HAPPE pipeline was that if there was any noise while collecting the data, rather than interpolating the signal or dropping out the channels, the entire dataset was discarded. We filter the data with a high pass filter from 1 Hz, which removes the non-stationary signal drift across the recordings. As mentioned in (Maess et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), under noisy conditions, as in our case of measurements obtained from experiments outside an electromagnetically shielded lab, the usage of a high-pass filter is the better option than detrending or baseline correction. Any noise or artefacts in the channels were checked for by using wavelet-enhanced ICA (wICA), as mentioned in (Gabard-Durnam LJ 2018).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eParticipants Profile\u003c/h2\u003e\n\u003cp\u003eThe distribution of intervention and control cases in the three different study locations and the sociodemographic profile of the sample is as given in Table\u0026nbsp;1.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSociodemographic Profile of the Study Samples\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIntervention (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eControls (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution of the study participants\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMahabubnagar\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (75.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (73.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98 (74.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGangtok\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (19.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (26.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (22)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBengaluru\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e04 (4.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e00 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e04 (3.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (36.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e07 (14.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (28.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52 (63.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 (85.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94 (71.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSchooling details\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePreschool (Playschool/LKG UKG)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e01 (1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e00 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e01 (0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrimary Education (1st to 5th grade)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e01 (1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (26.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (10.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMiddle school/Upper Primary school (6th to 8th grade)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43 (52.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (51.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68 (51.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary School (9th to 10th grade)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (45.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (22.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48 (36.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63 (76.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (73.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99 (75.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrban\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (23.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (26.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (24.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eChild lives with\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWith parents\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49 (59.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (26.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (47.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWith grandparents\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e04 (4.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e00 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e04 (3.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWith relatives\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e01 (1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e00 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e01 (0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWith caregivers (Hostel)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (34.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (73.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64 (48.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e82 (100)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e49 (100)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e131 (100)\u003c/strong\u003e\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\u003eThe age of the children, who participated in the study, ranged between 10 and 15 years, with the mean age of children being 13.08 years. About 71.75% of children were female, 75.6% were residing in rural areas. 47.32% of children were residing with their parents (59.75% of intervention and 26.53% of controls); 48.85% of children were residing in hostels (34.14% of intervention and 73.46 % of controls). 51.91% of children were in upper primary school; 36.64% were in secondary school; 10.69% of children were studying in primary school.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParent/ Caregiver Interview Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical analysis of the interview results of the parents and caregivers are listed in Table\u0026nbsp;2 for both the groups and also the significance values.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSummary of Parental Reports - Comparison of Intervention and Controls (Pre and Post)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eIntervention\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eControls\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF\u003cstrong\u003eeedback\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePre-test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePost-test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePre-test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePost-test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSig\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFocus\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (41.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (61.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19(38.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27(55.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.228\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003cp\u003e(41.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (26.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21(42.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17(34.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (17.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (12.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9(18.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(10.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eObservation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (50.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (605)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20(41.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24(51.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.643\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (38.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (20.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20(41.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17(36.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (11.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (13.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(06.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6(12.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMemory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (46.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (60.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19(38.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24(51.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.351\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (34.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (25.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22(44.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19(40.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (19.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (13.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(16.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e04(8.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-confidence\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33 (40.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49 (59.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23(46.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28(58.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.265\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (46.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (30.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22(44.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14(29.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (13.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (9.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.(8.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6(12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eParticipation in\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eactives\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOften\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (43.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48 (59.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.(38.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24(50.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.404\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSome time\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (34.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (25.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21(42.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19(39.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRarely\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (22.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (14.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9(18.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(10.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEmpathy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequently\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (44.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54 (66.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18(37.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27(56.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerately\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (28.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (19.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18(37.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15(31.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRarely\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (27.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11(13.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12(25.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6(12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eUnderstanding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eothers\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (41.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41(52.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16(34.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13(27.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.066\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (36.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29(37.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16(34.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27(57.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (22.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(10.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14(30.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7(14.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eI\u003cstrong\u003entuition\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (35.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47 (57.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15(30.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18(37.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.614\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (30.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26(53.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(52.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (22.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (12.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8(16.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(10.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComprehension\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (37.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 (52.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16(34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21(42.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.589\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (37.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (37.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26(55.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22(44.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (25.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (10.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5(10.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6(12.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAbstract Concept Understanding\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31(39.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55(67.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17(34.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23(46.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.082\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29(36.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17(20.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20(40.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22(44.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19(24.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10(12.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12(24.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4(8.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAmong the intervention group, statistically significant improvements were seen in child\u0026rsquo;s focus (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), empathy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), intuition (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), comprehension (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and child\u0026rsquo;s abstract concept understanding (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePsychometric Test Results\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eStatistical Significance Test for comparing mean values of Psychometric test results between Intervention and Control\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eIntervention (n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBase line\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEnd line\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBase line\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEnd line\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRaven\u0026rsquo;s SPM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMemory Recall (Nonverbal)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e84.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e116.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e87.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e109.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSusan Harter\u0026rsquo;s Test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e53.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e56.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.94\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\u003eStudy of psychometric test results shows that statistically significant improvements were seen in tests of MMSE (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and Memory profile (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for the intervention group. However, statistically significant improvements were also seen in the control group for Memory Profile (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;3).\u003c/p\u003e\n\u003ch2\u003eEEG Test Results\u003c/h2\u003e\n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;4, we show the power spectral density (PSD) plot of the signal collected from two electrodes located in the pre-frontal lobe (locations AF7 in purple and AF8 in green) for a child who underwent the BM program. This participant, a twelve-year-old female child, was randomly chosen from among all the children who had participated in the program. We do not normalize the power spectrum by spectral flattening or baseline normalization as we would like to compare the rate of the \u0026lsquo;1/f\u0026rsquo; drop for control and intervention.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eUnsupervised Learning Results\u003c/h2\u003e\n\u003cp\u003eIn this section, we learn and draw insights from the entire data by adopting some recent trends in unsupervised learning approaches. One such approach is the dimensionality reduction algorithm such as Principal Component Analysis (PCA). However, the data comprised of a mixture of both qualitative and quantitative variables.\u003c/p\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;5 (below), we show the scatter plot of the components after Factor analysis (Escofier Brigitte \u0026amp; Pag\u0026egrave;s J\u0026eacute;r\u0026ocirc;me (2008)) of the data which is a mixture of quantitative and qualitative. FAMD was chosen to include also the categorical variables along with the continuous variables. The columns that specified the location of the training program was removed prior to factorization, and only used for visualization and color labelling.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":" \u003cp\u003eIn recent years, literature shows a growing interest among researchers in studies related to early childhood development and the role of brain and cognition enhancement during the formative years of childhood. The objective of this article is aimed at exploring the hypothesis that multi-modal cognitive training can influence the inherent traits and bring a change in a child's external behavior. It is one of the first of its kind that used a controlled before and after study design to assess effectiveness of MCT among children under general settings, and evaluate their implications. The tests that are canvassed in this study were chosen to cover most of the spheres of cognitive abilities, because of its reliability and the validity tested in the previous research conducted (Madhavi and Jayanna, 2019).\u003c/p\u003e \u003cp\u003eThe parent and caregiver interviews in our study reported improvements in cognitive traits such as the child\u0026rsquo;s focus, empathy, memory, intuition, comprehension and understanding of abstract concepts, and improvements in behavior traits such as self-confidence, empathy, and participation. These findings are comparable to the results of the study conducted recently in \u003cb\u003e(\u003c/b\u003eMadhavi \u0026amp; Jayanna, 2019), who also reported within the educational settings.\u003c/p\u003e \u003cp\u003eThe Psychometric Test results revealed statistically significant improvements in the MMSE (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for the intervention group. However, for both the control and the intervention group, statistically significant improvements were seen for Memory (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). It had been reported (Nezla \u003cem\u003eet al\u003c/em\u003e., 2017.) that self-worth and self-competence in different settings like scholastics, social acceptance, athletic competence, physical appearance, and behavioral conduct improved with multimodal brain training programs. From our results, it could not be concluded if the BM program brought any significant difference in Memory, Raven\u0026rsquo;s SPM or Susan Harter\u0026rsquo;s Test to the intervention as against control participants. A similar conclusion can be drawn from the Violin Plots in Fig.\u0026nbsp;3, where we see significant improvements in MMSE but not the other tests.\u003c/p\u003e \u003cp\u003eOne possible explanation in the case of Susan Harter\u0026rsquo;s test is that it was observed that children had in general very low self-esteem scores. New findings on this subject believes that self-esteem is relatively stable trait and it is similar to some basic personality characteristics. Another reason for the difference could be attributed to the lower number of samples of Control than Intervention participants, and hence seeming to appear from a different statistical distribution. This might also be explained by a new theory (Jaeggi, S. M., 2020) that was put forth suggesting that the outcomes may be averaged across all individuals who received the intervention, and benefit experienced by a few might be averaged out. Another possibility is that the program does not improve fluid intelligence due to the shorter time span between training intervals (\u0026lt;\u0026thinsp;7days) (Wang, Z. et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The SPM results might be pointing out to the fact that while some abilities are enhanced after the training program, a few others might have impaired or diminished (Colzato, L. S. et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;4a is the PSD calculated on the baseline EEG signals for the control, and Fig.\u0026nbsp;4b was the PSD calculated from the processed EEG signal for the intervention after the eight weeks training program. It was reported that in the waking state with eyes closed, the frequency band below 20Hz scales as 1/f, and with possible peak around 20Hz, while in the slow-wave sleep state, the \u0026lsquo;1/f\u0026rsquo; scaling is not visible (Bedard et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). We observed a similar trend as well, except this difference was observed between the control and the intervention group. In the control group, the \u0026lsquo;1/f\u0026rsquo; scaling is visible (see Fig.\u0026nbsp;4a red line for local slope) but in the case of the intervention, the drop is much steeper than for the control (see Fig.\u0026nbsp;4b orange line for local slope). Our test results of EEG on a selected sub-sample of children, showed that the spectral signatures of the EEG in the intervention group were quite similar to those with spectra from EEG recorded during a relaxed or slow sleep state. We observed that, in general, the spectrum for the intervention group exhibited a drop in the higher frequencies, especially above 12 Hz (see Fig.\u0026nbsp;4b) frequencies. This raises the interesting reflection if the intervention program might have led to transfer effect on other skills like relaxation and a permanent change in their inner traits in comparison to the control group. It is known that Power-Law scaling governs the brain\u0026rsquo;s surface electric potentials (Miller et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Bedard et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and is also an intrinsic property of complex dynamic systems (Marković, D. and Gros, C. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Meisel, C., Bailey, K., Achermann, P. \u003cem\u003eet al.\u003c/em\u003e, 2017).\u003c/p\u003e \u003cp\u003eDuring FAMD, for visualization, we can see in Fig.\u0026nbsp;5, that the ellipses separate the two data coming from the two locations Mahabubnagar and Gangtok very distinctly. The location clustering could be an indicator to a hidden latent variable-the effect of the training facilitator (here teachers) on the training program. When we removed the influence of the locations in the analysis, for the data from the Gangtok site, there is a clear grouping happening between the Intervention and the Control groups (see Fig.\u0026nbsp;6) after the Factor Analysis. However, for the Mahabubnagar location (see Fig.\u0026nbsp;7), the distinction between the Control and intervention data is not distinct. This could be explained by the fact that the control group children from the Mahabubnagar program were living in hostels and the program leaked through the intervention group.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of the study\u003c/h2\u003e \u003cp\u003eThe study has a few limitations. Identification and recruitment of children who are naturally seeking admission into the Brighter Minds program was a challenge in study sites outside of Mahabubnagar. Fewer children were enrolling into the program in the learning centers on these sites. Even though we had identified and recruited 186 participants (93 intervention and 93 controls) in the baseline study, only 131 participants (82 intervention and 49 control cases) were available for end line assessment. The remaining cases (about 30%) were lost from the sample due to exposure of controls to intervention programs and drop out because of illness. This 30% loss was much higher than our anticipated loss of 10%. Many children, from the schools in Mahabubnagar district, were residing in hostels, and there are chances of exposure of the control participants to the intervention (see Fig.\u0026nbsp;7 and Fig.\u0026nbsp;8). It is also likely that some of the facilitating teachers from these rural schools do understand the importance of proving an environment that can improve the children\u0026rsquo;s confidence and self-esteem. The logistical complexity of conducting the controlled studies in community settings such as children's hostels will need to be taken into account in future studies. This also violated our initial assumption that the biological and social conditions during the study will not change or influence the study. It was also not possible to evaluate clearly what cognitive skill was impaired after the training program.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the EEG analysis, in order to simplify the recordings for children, we restricted the number of electrode sensors to 5 and were dry electrodes. Dry electrodes can bring in additional noise due to a temporary loss in contact with the skin. This was partly mitigated by using gel but we lost the signal from the dry electrodes and had to redo the experiments. As the number of electrodes are only a few, the recordings might not be precise for source localization using methods such as ICA. The recordings are done in a relatively noise-free room, but it is not shielded completely from outside noise. Advanced EEG analysis in a laboratory-controlled settings and functional MRI in future studies have potential to shed light on trait changes in the children that undergo cognitive and behavioural changes as a result of cognitive training.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusions","content":"\u003cp\u003eBrighter Minds' multimodal cognitive training program has a positive effect on self\u0026ndash;worth and self-competence as well as cognitive skills such as comprehension, observation, intuition, and focus. Ability of children for abstract thinking, verbal and spatial abilities, and mathematical reasoning when improved, will in turn benefit achievements and comprehensive abilities of children. The program duration and feasibility to implement in diverse settings including the schools, shows that the benefits of this program potentially outweigh the efforts. Early childhood education requires a paradigm shift as emphasized by India National Education Policy (2020) to advance human potential and development, and the present study provides new evidence and direction in this regard.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank all the parents and schoolteachers who provided their consent and helped in participation of children in the evaluation exercises. The authors would like to thank the Research team at Institute of Health Management Research, Bengaluru (IIHMR-B), Dr. S R Chandrasekhar Institute of Speech and Hearing, Center for Integrative Health and Wellbeing (CIHW) for supporting intervention and evaluation exercises of this research project, and Ashish Sinha (BITS Pilani, Goa) for the help with the EEG data processing.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe infrastructure and logistics required for the study was funded by Cognitive Skills Pvt Ltd. The donor did not have any role in the study design, study implementation, or data collection and analysis. All the authors involved in this study received funding from independent sources.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003ePP and KJ are unpaid research advisors at Brighter Minds. There are no other competing interests to declare for all other authors.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eMR:\u003c/strong\u003e Study designing, Methodology, Validation, Investigation, Report Writing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAJ:\u003c/strong\u003e Planning of psychometric tests, execution of psychometric tests, report writing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePP:\u003c/strong\u003e EEG Study Design, EEG Data Acquisition, EEG Formal Analysis, Statistical Analysis, Investigation, Curation, Data Visualization, Report Writing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGCK:\u003c/strong\u003e Statistical analysis, Report writing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUM:\u003c/strong\u003e Field planning, monitoring and supervision of data collection activities \u003cstrong\u003eKJ:\u003c/strong\u003e Report Review, Funding acquisition, Project conceptualization, Supervision\u003c/p\u003e\n\u003ch2\u003eEthics Approval\u003c/h2\u003e\n\u003cp\u003eInstitutional ethics committee of IIHMR University reviewed the proposal, research tools, and consent forms and provided ethical approval. Before reaching any intervention, it was ensured that all approvals from the authorities were in place. All ethical procedures were followed in guidance from the Institutional Ethical Review Board (IERB) of IIHMR University, Jaipur, India.\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u003c/h2\u003e\n\u003cp\u003eWritten informed consent, in local language was obtained from parents/caregivers. They were explained about the purpose of the study and that the confidential information collected will be masked.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the Mendeley repository here: https://data.mendeley.com/datasets/szf5gz82jp/1\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e[1] Bandla, S., Mandadi, G. D., \u0026amp; Bhogaraju, A. (2017). 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Neurosci. 12:353. doi: 10.3389/fnhum.2018.00353\u003c/p\u003e\n\u003cp\u003e[63] Zwilling, C. E., Daugherty, A. M., Hillman, C. H., Kramer, A. F., Cohen, N. J., \u0026amp; Barbey, A. K. (2019). Enhanced decision-making through multimodal training. \u003cem\u003eNPJ science of learning\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1), 1-10.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cognitive traits, behavior, EEG, multimodal cognitive training, unsupervised learning","lastPublishedDoi":"10.21203/rs.3.rs-468572/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-468572/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this article, we evaluate the hypothesis that a multimodal cognitive training (MCT) program, the Brighter Minds, can enhance certain inherent traits of a child and thus bring changes in the external behavior. For the study, 186 children (randomized to 93 each in intervention and control group) aged 10-15 years were enrolled from three different locations. Psychometric tests, parental/caregiver interviews and EEG (electroencephalography) tests were conducted before and after the program. Intervention group showed strong statistical significance for improvements in Mini Mental Status Examination (MMSE) (P\u003c0.01) but no significance for Raven’s Standard Progrssive Matrices (SPM) or Susan Harter’s test. The parental/caregiver reported satistically significant improvements in focus (P\u003c0.05), empathy (P\u003c0.05), intuition (P\u003c0.05), comprehension (P\u003c0.05) and understanding of abstract concepts (P\u003c0.05) for the intervention group. For the control, Power Spectral Density (PSD) of the baseline eyes-closed (EC) EEG recording, the spectrum below 20Hz exhibited the characteristic “1/f” spectral scaling of the power-law. This signature matches prior reported evidence in literature of those in wakeful state with EC. The intervention group EC PSD, however, exhibited a signature similar to those in a slow sleep state; reflective of the possible transfer effect of the training on other skills like relaxation. We used unsupervised learning methods with dice distance, on the psychometric and interview data, to show the effect of location and the exposure of a few control children to the program.","manuscriptTitle":"Impact of Multimodal Cognitive Training on Cognitive Traits of Children: A Multicentric Interventional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-30 12:00:06","doi":"10.21203/rs.3.rs-468572/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"267e1b97-74d6-46aa-8319-869af3d8a85b","owner":[],"postedDate":"April 30th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":4009451,"name":"Biological sciences/Neuroscience"},{"id":4009452,"name":"Biological sciences/Psychology"},{"id":4009453,"name":"Physical sciences/Mathematics and computing/Computational science"}],"tags":[],"updatedAt":"2021-09-06T10:29:05+00:00","versionOfRecord":[],"versionCreatedAt":"2021-04-30 12:00:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-468572","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-468572","identity":"rs-468572","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}