Neurocognitive Assessment of Attention and Its Relationship With Academic Performance Among Schoolchildren

Neurocognitive Assessment of Attention and Its Relationship With Academic Performance Among Schoolchildren | 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 Neurocognitive Assessment of Attention and Its Relationship With Academic Performance Among Schoolchildren Bishal Joshi, Jay Prakash Jha, Yadwendra Yadav, Bikash Kumar Agrawal, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8223950/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version Abstract Background: Attention is a key cognitive factor in classroom learning, but the relationship between attentional abilities and academic performance remains understudied in South Asian countries like Nepal. This study aimed to assess attentional performance by the Stroop Color Word Test (SCWT) and Trail Making Test (TMT) and its relationship with academic performance among schoolchildren in Bhiarahawa, Nepal. Methods: In this cross-sectional study, 377 students from grades 6 to 10 were included using stratified random sampling from three schools. Neurocognitive assessment of attention was done by SCWT (Word, Color, Color-Word), with calculated Interference score (IG) and TMT-A and TMT-B. Academic performance was taken from the official school mark-sheet (overall percentage and subject scores). Sociodemographic data, anthropometrics, and screen use habits were collected. Associations among the variables were evaluated with Pearson correlations and multiple linear regression analysis. Results: The Stroop test score in 45 seconds was Word 82.3 ± 17.86, Color 56.17 ± 11.52, Color-Word 34.3 ± 8.2, with a calculated interference score (IG) of 1.1 ± 6.8, whereas the time for TMT-A was 37.1 sec (±12.6); TMT-B was 80.9 sec (±31.4). There was a significant inverse relationship between TMT A and TMT-B completion times and overall academic percentage and subject scores, signifying that faster TMT performance corresponds to better academic performance, especially in mathematics, as confirmed by regression (β = -0.30 for TMT-A and β=-0.34 for TMT-B). In contrast, the SCWT IG score had no significant association. Conclusion: In this study, TMTs were the robust correlates of the academic outcomes, particularly mathematics, whereas the Stroop test was not a significant predictor of academic achievement. These findings emphasize that school-based interventions for enhancing the attentional abilities of students can greatly support learning. In the future, longitudinal studies using culturally adapted measures are recommended. attention Trail making test Stroop academic performance schoolchildren Nepal Introduction Attention, a gateway to learning, is a basic cognitive skill necessary to engage in information while ignoring other irrelevant stimuli. In an academic context, the ability to concentrate on the instructional materials is critical for knowledge acquisition and retention. 1 The attentional abilities are developed significantly in early ages of life and are foundational for goal-directed learning. According to Posner’s attentional network theory, 2 the key components of attention in humans include three interconnected subsystems: the alerting network, which maintains sustained attention, required for continuing long term vigilance, the orienting network responsible for the selective attention to concentrate in a specific stimulus enabling students to focus on study material, and executive attention network allowing goal directed behaviors and conflict management. These attentional activities are primarily governed by the prefrontal cortex and associated regions of the brain and are recognized as crucial determinants of academic success. 3 Impairment in these subsystems, including attention deficit and poor inhibitory control, can impair information acquisition, memory processing, and consolidation, leading to disruptions in academic success. 4 Among different systems, sustained attention is found to enable long-term engagement of students with lessons, while selective attention enables the purposeful selection of specific information in the face of classroom distractions, and executive attention helps in the execution of complex tasks in the context of competing cognitive demands. 5 Studies have demonstrated that students with better attention performance are better in core learning fields like language, arts, and mathematics. 6 , 7 For example, Rabiner et al. (2016) observed that students of first grade with attention problems had poorer growth in mathematics and reading abilities compared to the other students without such problems. 8 Similarly, in a study including 700 school students, Gallen et al. found that attention was positively related to the performances on reading comprehension and math fluency as well as overall educational measures like statewide standardized test scores. 1 In a Meta-analysis including a review of 21 studies (n = 7,947), it was reported that executive function (of which attention is a core component) had a moderately significant relationship with academic achievement, especially mathematics. Collectively, these findings show that attentional abilities have a direct correlation with academic outcomes in school students. 9 Among various neurocognitive performance tests measuring these abilities, the Stroop Color and Word Test (SCWT) and Trail Making Test (TMT) are widely used due to their validity and reliability. 10 The SCWT is extensively used to evaluate the selective attention and inhibitory control by measuring the cognitive interference that occurs when the participants name the ink color of the words that denote conflicting colors. 11 Likewise, TMT part A measures the visual scanning, processing speed, and sustained attention, whereas part B assesses the executive attention and executive functions, including cognitive flexibility, inhibitory control, and working memory. 12 These neurocognitive tests are particularly useful in school students, as they help identify attention deficits in early ages, which may impact the learning abilities of children. In South Asian contexts, including Nepal, various confounding factors such as study pressure and lack of resources, nutritional issues, and digital distractions can also moderate the relation between attention and learning. For example, Nepal’s academic environment often places a high value on academic achievement, and underperformers may be treated unfavorably and may go through harsh punishment at home and school, and even the children with attention problems may be stigmatized or punished for poor performance. 13 Mental health research in Nepal indicates that the prevalence of attention deficit hyperactivity disorder (ADHD) is common: one hospital-based study found this prevalence to be 11.7% among school children, suggesting that Nepali students struggle with attention deficit and associated executive function impairments. 14 Apart from that, other regional factors such as crowded classrooms, limited special education, and cultural attitudes towards discipline are the things to be considered while studying the correlation between attention and academic achievements in Nepali School children. Moreover, nutritional deficiency among the children with low BMI. 15 and high engagement in mobile phones and screens exacerbating distractions, needs localized investigations. 16 Modifiable lifestyle factors have been seen to positively impact attentional abilities. For instance, researchers have observed that children engaged in leisure-time physical activity and reading for at least one hour on weekdays demonstrate significantly better attention scores, independent of socioeconomic factors. 17 Furthermore, cognitive training, including memory exercises and computerized games found to have a positive impact on attentional and academic performances. 18 , 19 Similarly, studies have shown that regular physical exercise and mindfulness meditation training boost sustained and selective attention in children. 20 , 21 Despite the evidence from the Western and some Asian contexts linking attention with academics, there remains a gap in the South Asian population, and even the complete absence of studies focused on this topic in Nepal. This study, conducted in Bhairahawa, Nepal, among the school children of 6 to 10 grades, tries to find these voids by investigating the relationship between attention as assessed through SCWT and TMT, and academic performances, incorporating modifiable factors like screen use and undernutrition as measured by BMI. We hypothesize a positive correlation between SCWT scores (indicating better selective and executive attention) and a negative correlation of TMT score (increase in time reflecting poorer sustained and executive attention) with marks secured in the exam. This research contributes novel data on attention academic links in the Nepali context, highlights modifiable risk factors, and advocates the target strategies to mitigate better learning. Material and methods 2.1 Study design and setting This cross-sectional, observational study was conducted among school-going children of Bhairahawa, Lumbini province, Nepal. It was conducted in two private and one government secondary school from October 2024 to February 2025 following the ethical approval from the Institutional Review Committee of Universal College of Medical Sciences (UCMS), Bhairahawa (UCMS/IRC/098/24). 2.2 Sample size calculation Sample size was calculated using Fisher's z transformation to find the correlation between attentional test performance and academic scores. Following formula was used: n = ((Zα/2 + Zβ) / (0.5 × ln [(1 + r) / (1 − r)]))² + 3 Where: r= expected correlation Zα/2=standard normal value for two tailed α Zβ= normal deviate corresponding to desired power In our study, we assumed a conservative expected correlation of r=0.205 from a similar previous study, 22 two-tailed α=0.05, and statistical power, 80% (Zβ=0.84) and Zα/2=1.96. While putting the values in the formula, the minimum required sample size was approximately n=184 participants. To allow for clustering by school, planned multivariate analyses, and potential missing data, the sample was increased, and a total of 377 students were finally included in the study. 2.3 Participant selection and sampling The participants of the study comprised male and female students in grades 6 to 10. A stratified random sampling technique was employed to ensure the representativeness. For selection of schools, we obtained a list of schools from the district education office and applied simple random (lottery method), selecting three secondary schools among a total of 34 schools of Bhairahawa. Within each selected school, students were stratified by grade, and the required number of participants from each grade was determined proportionally to the total enrollment size. In grades nine and ten in one of these two private schools, two sections were present, so data collection was done from both sections. In each class, 30 students were randomly chosen using a lottery method to participate in the study. After selection, information sheets and assent forms were distributed to selected students, along with the parental consent forms. Data collection was conducted on the following day only from those participants who met the following inclusion and exclusion criteria. Inclusion criteria Students enrolled in 6 to 10 grades. Those who provided signed parental/guardian consent and provided their own assent. Students present on the scheduled day of data collection. Exclusion criteria Known cases of psychiatric, neurological, and developmental disorders Visual or motor impairments that would prevent valid completion of TMTs. Color deficiency as identified by the Ishihara color plate (for SCWT suitability) Not willing to take part in the study 2.4 Measures A case proforma was used to record sociodemographic data, including, age, sex, dietary habits, and medical history. Height was measured with stadiometer and weight was taken by digital weighing machine, and BMI was calculated (kg/m 2 ) from the obtained values. The BMI was classified as thinness (undernutrition), normal, overweight and obese as per the WHO classification for children. 23 Academic performance was assessed from the aggregate marks (%) from the most recent final school examination (the last academic year). Marks were obtained from the record section of the schools with permission. The overall percentage and subject-wise scores were recorded for the analysis. Stroop -Color Word Test (SCWT) Stroop- Color Word Test was used as a tool for assessing Selective attention and inhibitory control. For the Stroop test, three subtasks were administered using the paper test format, and a timer of 45 seconds was set for each subtask. In the Word test, the participants were instructed to read as many words (W) as they could. In the Color test (C), the participants were instructed to name the color. In the Color/Word test, they were instructed to read the ink color while ignoring the written words. The number of correct responses per subtest was recorded. Interference Score (IG) was calculated using the formula IG = [CW-W*C/(W+C)]. The SCWT procedure and scoring technique follow the established descriptions 11 Trail Making Test (TMT) Selective attention, sustained attention, and other executive functions 24 (which include interference control, cognitive flexibility, and working memory) were evaluated by the Trail Making Tests (TMT). First of all, the participants were given proper instructions regarding the examination. In TMT: A, they were instructed to join the numbers from 1 to 25 on a piece of paper using a pencil, and the time taken was noted. Similarly, in TMT: B, the participants were instructed to join numbers and letters alternately as 1 A, 2 B, and so on up to 13. The time taken to complete the task was recorded for each part. The participants were not allowed to lift the pencil off the paper, and time penalties were noted as per standard administration and interpretations follow standard guidelines and normative references. 25 2.5 Testing procedures Data collection was conducted in a quiet, well-lit room in the assembly halls of the schools during regular school hours. After verifying the consent/assent forms and collecting demographic and anthropometric data, tests were administered in the following order: Stroop test (W, C, CW), then TMT-A and TMT-B. Each participant completed the test individually under the supervision of the investigator in a one-to-one setting. All the investigators were trained in standardized instructions, error-handling rules, and timing procedures prior to the real investigation. A pilot study of 20 students was performed to verify comprehension for the Stroop test, standardize timing, scoring, and data recording. These samples were not included in the final data. Minor adjustments were made to the clarity of verbal instructions based on pilot feedback. All the raw data were double-checked by a second investigator for data entry accuracy. 2.6 Statistical analysis Data were entered into Microsoft Excel 2010 and analyzed using IBM SPSS Statistics 20.0. Descriptive statistics were used to summarize participant characteristics. Categorical variables were presented as frequencies and percentages, while continuous variables were expressed as mean ± standard deviation (SD) with normality assessed via the Shapiro-Wilk test. Pearson correlation coefficients were computed between Stroop IG, Trail A, Trail B, academic subject scores, overall scores, BMI, and class. Multiple linear regression models predicted cognitive outcomes (Stroop IG, Trail A, Trail B times) from academic scores (Math, English, Science, Nepali, and overall percentage). All analyses were two-tailed, with statistical significance set at p < 0.05. Results Out of a total of 764 students, 377 students from grades 6 to 10 were included in the study. Table 1 describes the general characteristics of the students participated in the study. Sex ratio was 0.68, favouring males. More than half of the students participated in grades 9 and 10. Most of the children were non-vegetarian, and the majority of them had no significant medical history. Among those having a history, hypothyroidism, vertigo, appendicitis, menstrual disturbance, jaundice, myopia, and seasonal allergy were common. More than half (56%) of children report using some device (mostly a smartphone) at bedtime, and its use at meal time was also common in over a quarter of them. About a fifth (18.57%) of the participating children were under the category of underweight, i.e., their BMI was lower than 2 SD below the median BMI for their age, and about 10% were overweight or obese (BMI higher than 1 SD above the median BMI for their age) as per the WHO classification. Table 1. General characteristics of participants and their frequency (n=377) Parameters Frequency Percent Gender Female 153 40.6% male 224 59.4% Grade 6 59 15.6% 7 50 13.3% 8 64 17.0% 9 109 28.9% 10 95 25.2% Diet Vegetarian 52 13.8% Non-vegetarian 325 86.2% Medical History Not significant 360 95.5% Some history 17 4.5% Bedtime Screen use No screen at bedtime 166 44.0% screen used at bedtime 211 56.0% Screen time per day for entertainment 4 hour 24 6.4% Screen use at meal time Never 133 35.3% Rarely 41 10.9% Sometimes 107 28.4% Often 21 5.6% Always 75 19.9% Body Mass index Underweight 70 18.57% Normal 270 71.62% Overweight 28 7.43% Obese 9 2.39% Table 2 shows the descriptive values of anthropometric and cognitive function. The mean age of participants is 14.42 years (SD = 1.65), with a range of 8-18 years. The average height, weight, and BMI indicate that most students fall within normal ranges. The Stroop test results show moderate levels of performance in word reading (82.3 ± 17.86 words read in 45 sec), colour naming (56.17 ± 11.52 colours named in 45 sec), and colour-word interference (34.3 ± 8.2 colours named in 45 sec in the incongruent list). The IG score suggests high variability across tasks; about half (175, 46.42%) of the students got a negative IG score as per the calculation, making the total mean score just 1.1. Table 2. Descriptive characteristics of participants (n=377) Descriptive Statistics Mean ± SD Range Age (years) 14.42 ± 1.65 8 - 18 Height (cm) 158.39 ± 10.27 127 - 181 Weight (Kg) 46.09 ± 9.55 21.9 - 83 BMI (Kg/m 2 ) 18.3 ± 3.19 11.6 - 32.02 Stroop test Word (number) 82.3 ± 17.86 15 - 128 Stroop test Colour (number) 56.17 ± 11.52 14 - 96 Stroop test Colour-word (number) 34.3 ± 8.2 10 - 61 Stroop Test IG socre 1.1 ± 6.83 -27.48 - 25.33 Trail A (second) 37.11 ± 12.63 6.14 - 99.6 Trail B (second) 80.86 ± 31.43 22.8 - 285.62 Bivariate correlation between different numerical parameters is shown in Table 3. It shows that the Trail A and Trail B tests are significantly negatively correlated with all the academic scores (p0.05). Table 3. Bivariate Pearson correlation between different parameters of cognitive test scores, academic scores, age, BMI and class (grade) in school. For each parameter, first row shows correlation coefficient, and second gives corresponding p value for the coefficient. Trail A Trail B Math English Science Nepali Overall Score Age BMI Class Stroop IG score 0.024 -0.047 -0.007 -0.006 -0.016 -0.07 -0.037 0.038 0.045 -0.01 0.636 0.364 0.893 0.906 0.758 0.174 0.475 0.464 0.381 0.845 Trail A 1 .440* -.301* -.287* -.243* -.298* -.187* -.176* -0.095 -.166* <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.001 0.066 0.001 Trail B 1 -.198* -.179* -.129* -.137* -.110* -.177* -0.021 -0.084 <0.001 <0.001 0.012 0.008 0.033 0.001 0.678 0.102 Math 1 .799* .831* .834* .817* .177* 0.096 .155* <0.001 <0.001 <0.001 <0.001 0.001 0.062 0.003 English 1 .845* .808* .748* .152* .104* .136* <0.001 <0.001 <0.001 0.003 0.043 0.008 Science 1 .832* .794* .107* 0.042 .105* <0.001 <0.001 0.037 0.414 0.042 Nepali 1 .748* .109* .121* .116* <0.001 0.034 0.019 0.025 Overall score 1 -0.008 0.01 .110* 0.88 0.845 0.033 Age 1 .150* .632* 0.003 <0.001 BMI 1 .148* 0.004 * Correlations significant at 95% confidence. To examine the relationships between academic subject scores and cognitive test performance, we conducted a series of linear regression analyses (Table 4). We used separate models to predict each cognitive test score (Stroop IG, Trail A, and Trail B as dependent variables) from the academic subject scores (Math, English, Science, Nepali, and overall percent score, as independent variables). We assumed that the data met the standard assumptions of linear regression, including linearity, independence, homoscedasticity, normality of residuals, and little to no multicollinearity. For the Stroop IG score, only the Nepali score was a significant predictor (β = -0.23, p = 0.030), with a small effect size (R² = 0.02). The model was not significant (F = 1.28, p = 0.271). For Trail A as the dependent variable, Math (β = -0.30, p = 0.007) and overall academic percent score (β = 0.22, p = 0.016) were significant predictors, with a moderate effect size (R² = 0.12). The model was significant (F = 10.12, p < 0.001). Similarly, for Trail B, Math (β = -0.34, p = 0.003) was a significant predictor, with a small to moderate effect size (R² = 0.06). The model was significant (F = 4.42, p = 0.001). Specifically, for every one-unit increase in Mathematics score, there is a corresponding 0.30-point decrease on the Trail A test (p = .007) and a 0.34-point decrease on the Trail B test (p = .003). The other subject scores did not show significant relationships with any cognitive performance. Table 4. Multiple linear regression analysis of three cognitive scores in relation to the students’ academic performance (exam scores) Stroop Test IG score R R Square Adjusted R Square Std. Error of the Estimate .13 .02 .00 6.61 Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 2.48 1.27 .00 1.96 .051 Math .05 .03 .17 1.47 .141 English .04 .04 .12 1.10 .273 Science .00 .04 .01 .10 .923 Nepali -.08 .04 -.23 -2.18 .030* Total Score -.03 .03 -.09 -.93 .354 Trail A score R R Square Adjusted R Square Std. Error of the Estimate .35 .12 .11 11.94 Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 44.51 2.29 .00 19.46 .000* Math -.16 .06 -.30 -2.71 .007* English -.11 .06 -.18 -1.77 .077 Science .07 .07 .12 1.07 .284 Nepali -.11 .06 -.17 -1.70 .090 Total Score .15 .06 .22 2.43 .016* Trail B Score R R Square Adjusted R square Std. Error of the estimate .24 .06 .04 30.73 Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 88.00 5.89 .00 14.95 .000* Math -.47 .15 -.34 -3.04 .003* English -.31 .16 -.20 -1.89 .059 Science .18 .17 .12 1.03 .304 Nepali .16 .16 .10 .95 .342 Total Score .25 .16 .15 1.58 .116 For each model of cognitive score, independent variables are academic scores of Math, English, Science, Nepali and total score. * Significant at 95% confidence. These regression findings suggest that academic subject scores are associated with cognitive test performance, especially in Trail A and Trail B, but not of Stroop IG score. However, the effect sizes are small, and the models do not explain a large proportion of the variance in the cognitive test scores. Discussion In this cross-sectional study, 377 schoolchildren of grades 6-10 from Bhairahawa, Nepal, were assessed to find out the relationship between attentional abilities and their academic performance. The neurocognitive assessments of attention were done by the Stroop Color Word Test (SCWT) and the Trail Making Test (TMT), and academic performance was assessed through the aggregate marks (%) from the most recent final school examination of the last academic year. The result showed that there was a significant negative correlation between the time taken to complete TMT-A and TMT-B and academic performance. This illustrates that slower performance on these tests, reflecting poorer sustained attention, processing speed, and executive attention, was linked to lower grades in the examination. In our findings, the scores secured in mathematics were the strong predictors in regression models for both TMT-A (β = -0.30, p = 0.007) and TMT-B (β = -0.34, p = 0.003), with overall academic percentage also significant for TMT-A (β = 0.22, p = 0.016). Whereas, there was no significant association between interference measure (IG Score) and academic performance, apart from a weak negative relationship with Nepali scores in regression (β = -0.23, p = 0.030), though the model was not significant. Moreover, the use of screens during bedtime was prevalent (56%) among students, and was more common among the boys, while almost one fifths of the children were underweight, highlighting the potential confounding factors influencing cognition. The findings of the present study are in alignment with the previous studies, which have established the association between attentional abilities and academic performance. The inverse relationship between TMT and academic performance is consistent with other studies 26-29 showing that longer TMT completion times predict poorer academic performance in children, as TMT taps into executive functions necessary for problem-solving skills in subjects like mathematics. In a large school-based study of children, Gallen et al. found that sustained attention performance was positively linked with both math fluency and reading comprehension, along with standardized test scores. 30 Similarly, in a meta-analysis including 21 studies (n=8000) in 6-12-year-old students, the researchers found that there is a moderate pooled correlation between general executive function and academic performance, with a slightly stronger effect on mathematics than language. The results of the present study in the Nepali context, indicating math scores as the most predictive of TMT performance, are consistent with these findings, supporting the notion that better attentional control enhances mathematical problem-solving ability. However, the present study found no significant association between Stroop test Interference Score (IG) and academic outcomes contrasting with some previous studies. For instance, the results reported by Dvorak (2024) 31 showed a weak but statistically significant correlation between the Stroop Effect and academic performance in the University of Sweden. Similarly, in another Iranian study, a statistically significant relationship was found between the Stroop test and academic achievement 32 suggesting that the cognitive capacity of inhibitory control as measured by this test may be a predictive factor for academic achievements. Unlike these studies, our sample from Nepali school children showed no relationship between the Stroop test and academic performance. There could be several reasons behind this outcome. First, it could be due to the developmental differences, as many prior studies included older adolescents or university students whose inhibitory control and interference resolution are more mature, whereas our participants consisted of younger schoolchildren whose neurocognitive pathways for overcoming the interferences in the Stroop effect are still developing. Second, the issues regarding the task performances and language factors may also play a role, as our Stroop test was administered in English, and even though students were familiar with English as a medium of instruction, it remains a non-native language for Nepali students, which could lead to reading proficiency confounds. Finally, the contextual factors, including teaching methods, large class size, syllabus focus, emphasis on rote learning education, and differences in examination patterns, could produce the impact. Indeed, other studies have shown an inconsistent relationship between SCWT and academic outcomes, reflecting that Stroop performance can be influenced by reading ability and motivation apart from pure inhibitory control. 33,34 Theoretically, our results can be interpreted through Posner’s framework of attentional networks,2 where alerting (TMT-A) sustains the attention on a desired task, orienting (SCWT Color) selects relevant inputs, and executive attention (TMT-B and SCWT IG) resolves the interferences during the complex learning process. So, TMT and SCWT recruit partly overlapping but distinct attentional mechanisms due to different neuronal circuits. While exploring in detail, TMT-A mainly measures visual processing, visuomotor scanning, and processing speed, which are the neuronal functions of dorsal attention and occipito-parietal sensorimotor pathways; TMT-B needs an alternating task that substantially engages dorsolateral prefrontal cortex, anterior cingulate, and broader fronto-parietal control systems that also require the maintenance of sequence information via working memory. 35 Slower TMT times reflect the compound phenomenon, including decreased processing speed, limited maintenance of working memory, and less efficient fronto- parietal coordination. Large pediatric normative studies show a clear age-related decrease in TMT-A and TMT-B completion times. 36-38 These findings are quite consistent with the findings of our studies, as it shows a statistically significant negative correlation of age with both the trail making test times. This demonstrates that as children grow older, maturation of fronto-parietal networks supports faster processing speed, improved sustained and selective attention, and more efficient cognitive control, leading to shorter completion times. Furthermore, while comparing with the previous studies, it was revealed that typical TMT-A and TMT-B times for mid adolescents are often somewhat lower than the mean of our sample of both the Trail tests. 35,37 This difference is attributable to regional, educational, nutritional or testing familiarity factors. In contrast, the Stroop Color Word Test IG score is a classical measure of cognitive interference and engages a neuronal network of conflict monitoring and resolution. To execute this mechanism, the anterior cingulate cortex signals response conflict, and the lateral prefrontal region (particularly dorsolateral and ventrolateral PFC) implements the top-down suppression of the automatic tendency to read the word rather than name the ink color. 38 Moreover, recent neuroimaging evidence has identified a cross-hemispheric, excitatory-inhibitory loop between the left lateral PFC and the right cerebellum. During Stroop interference, the PFC appears to enhance the activity of the cerebellum, whereas the cerebellum exerts inhibitory feedback on the PFC, maintaining a dynamic balance leading to refinement in conflict resolution and timing of response suppression. 39 Developmental studies have further postulated that ACC and prefrontal cortex activation are responsible for these interference resolution increases with age, reflecting maturation of top-down control systems through childhood to adolescence and reaching to its optimum level in adulthood. 40 In the present study, the mean value of IG=1.1 ± 6.83 is small and close to zero, indicating a low average interference score, but the variability was very high, ranging from -27.48 to 25.33, indicating that there is partial maturation of interference control in early adolescence. This can also be due to measurement compression because the test was administered in English, which remains a non-native language as discussed earlier, or may be because the difference between the Color-word and Word condition may have been reduced in our sample making the interference score less sensitive to pure inhibitory control. 41 Indeed, previous research conducted in the Nepali population also reveals that administration format and language affect Stroop measures 42 and several validation efforts recommend administering the task in students’ native language. 43,44 In our study, we also explored the potential contribution of screen exposure and nutritional status as measured by BMI to attention and academics. Over half of our participants reported bedtime device use (with boys more affected), which is consistent with the global scenario and can be one of the factors disrupting sleep and attention, and indirectly impacting academics. 45 In the South Asian context, the penetration of smartphones is very high, and a study conducted in India has shown the clear link between excessive screen time and reduced executive function, decreased academic performance. 46 Moreover, it was observed that almost one-fifth of students were underweight, which may have further complicated the true relationship between attention and academic performance. Nutrition status has been linked to cognitive development among the children of Nepal, with deficient nutrition being associated with lower scores in cognitive examinations in children, as highlighted in Rupandehi (a district in which our study area falls), Nepal. 47 Similarly, another study in Nepal has also highlighted that malnutrition prevalent in Nepali children due to poor socioeconomic status may have some impact on cognitive development. 48 Taken together, these findings suggest that high screen exposure and BMI are potentially modifiable factors that could impair attention and academic performance. Thus, at the level of school, interventions targeting healthy nutrition and enhancing awareness regarding limiting screen time may help enhance attention and, hence, the academic outcomes to some extent. The strength of the study includes a large, multi-school sample and standardized testing; key limitations are cross-sectional design and English Stroop administration. Future work should use longitudinal designs, native language Stroop variants, and broader cognitive batteries. Producing such locally grounded evidence also helps strengthen Nepal’s research voice and reduce epistemic dependency. 49 Conclusion This study examined the attentional abilities and their links to academic performance in Nepali schoolchildren, finding a significant negative correlation between Trail Making Test (TMT) times and grades in the exam, particularly in mathematics, reflecting that processing speed, sustained attention, and executive attention were consistently associated with higher academic scores. There was no robust association between Stroop interference scores and academic achievements, likely because of developmental and language influences. The findings highlight the role of attention in education and suggest that schools might boost learning by supporting attention (e.g., brief attention training, structured physical exercise, and demotivating screen use). Abbreviations ACC - Anterior cingulate cortex BMI - Body mass index CW - Color-Word condition of Stroop test DLPFC - Dorsolateral Prefrontal Cortex EF - Executive functions IG - interference score (Stroop Color-Word interference) PFC - Prefrontal Cortex SCWT - Stroop Color-Word Test SD - Standard deviation TMT-A -Trail Making Test Part A TMT-B - Trail making test Part B Declarations Ethics approval and Consent to Participate This study received ethical approval from the Institutional Review Committee (IRC) of Universal College of Medical Sciences, Bhairahawa Nepal (Reference No: UCMS/IRC/098/24). Permission to conduct the study was obtained from all participating school. Written informed consent was obtained from parents/guardians of all participants students, and written assent was obtained from the students themselves prior to data collection. All the procedures were conducted in accordance with the Declaration of Helsinki and relevant national guidelines. Consent for Publication Not applicable Availability of data and materials The datasets generated and analyzed during the current study are not publicly available but will be available from the corresponding author (B.J.) on reasonable request. Competing Interests The authors declare that they have no competing interests Funding No funding was received for this study Authors' contributions B.J. designed the research protocol, coordinated data collection, performed the primary data analysis, and manuscript writing. J.P.J. performed overall data analysis, supervised the overall methodology, guided statistical analysis, and provided critical revisions to the manuscript. Y.Y. and B.K.A. assisted in data acquisition, data management and preliminary analysis. S.A.M., A.T., A.K., and L.S. contributed to field coordination, data collection, quality assurance and manuscript editing. All authors reviewed, contributed to, and approved the final manuscript. Acknowledgements We would like to thank all the participants from all the three schools who took part in our study in an enthusiastic way. We would also like to thank administrations of all the schools for making smooth arrangements for the data collection team. References Gallen, Courtney L., et al. "Contribution of sustained attention abilities to real-world academic skills in children." Scientific reports 13.1 (2023): 2673. Posner MI, Rothbart MK. Research on attention networks as a model for the integration of psychological science. Annu. Rev. Psychol.. 2007 Jan 10;58(1):1-23. Bunge SA, Dudukovic NM, Thomason ME, Vaidya CJ, Gabrieli JD. Immature frontal lobe contributions to cognitive control in children: evidence from fMRI. Neuron. 2002 Jan 17;33(2):301-11. Areces D, Dockrell J, Garcia T, Gonzalez-Castro P, Rodriguez C. Analysis of cognitive and attentional profiles in children with and without ADHD using an innovative virtual reality tool. 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The Clinical Neuropsychologist. 2025 Aug 18;39(6):1696-721. Oksuz S, Depreli O, Ozdil A, Iyigun G, Angın E, Bilgin BF, Karaman A. Comparison of Cognitive Functions, Sleep Quality, Emotional State, Balance and Physical Activity Levels of University Students According to Academic Success. Internet Journal of Allied Health Sciences and Practice. 2025;23(2):2. Waggestad TH, Kirsebom BE, Strobel C, Wallin A, Eckerström M, Fladby T, Egeland J. Improving validity of the trail making test with alphabet support. Frontiers in Psychology. 2023 Jul 27;14:1227578. Gallen CL, Schaerlaeken S, Younger JW, Anguera JA, Gazzaley A. Contribution of sustained attention abilities to real-world academic skills in children. Scientific reports. 2023 Feb 15;13(1):2673. Dvorak M. Inhibitory control and academic achievement–a study of the relationship between Stroop Effect and university students’ academic performance. BMC psychology. 2024 Sep 27;12(1):498. Soltani Nezhad M, Delroba M. Investigating the relationship between students’ executive functions, emotion regulation, and academic achievement. Discover Psychology. 2024 Sep 18;4(1):120. Protopapas A, Archonti A, Skaloumbakas C. Reading ability is negatively related to Stroop interference. Cognitive Psychology. 2007 May 1;54(3):251-82. Varjacic A, Mantini D, Demeyere N, Gillebert CR. Neural signatures of Trail Making Test performance: Evidence from lesion-mapping and neuroimaging studies. Neuropsychologia. 2018 Jul 1;115:78-87. Arango-Lasprilla JC, Rivera D, Aguayo A, Rodríguez W, Garza MT, Saracho CP, Rodríguez-Agudelo Y, Aliaga A, Weiler G, Luna M, Longoni M. Trail making test: Normative data for the Latin American Spanish speaking adult population. NeuroRehabilitation. 2015 Nov 26;37(4):639-61. Tombaugh TN. Trail Making Test A and B: normative data stratified by age and education. Archives of clinical neuropsychology. 2004 Mar 1;19(2):203-14. Gündüz H, Gündüz GB, GÜLVEREN H, TAVAT BC. Norm determination study of trail making test, enhanced cued recall test and clock drawing test for Turkish sample between 6-18 years of age. Archives of Neuropsychiatry. 2021 Aug 2;58(4):314. Peterson BS, Skudlarski P, Gatenby JC, Zhang H, Anderson AW, Gore JC. An fMRI study of Stroop word-color interference: evidence for cingulate subregions subserving multiple distributed attentional systems. Biological psychiatry. 1999 May 15;45(10):1237-58. Okayasu M, Inukai T, Tanaka D, Tsumura K, Shintaki R, Takeda M, Nakahara K, Jimura K. The Stroop effect involves an excitatory–inhibitory fronto-cerebellar loop. Nature Communications. 2023 Jan 11;14(1):27. Adleman NE, Menon V, Blasey CM, White CD, Warsofsky IS, Glover GH, Reiss AL. A developmental fMRI study of the Stroop color-word task. Neuroimage. 2002 May 1;16(1):61-75. Rivera D, Morlett-Paredes A, Peñalver Guia AI, Irías Escher MJ, Soto-Añari M, Aguayo Arelis A, Rute-Pérez S, Rodríguez-Lorenzana A, Rodríguez-Agudelo Y, Albaladejo-Blázquez N, García De La Cadena C. Stroop color-word interference test: Normative data for Spanish-speaking pediatric population. NeuroRehabilitation. 2017 Oct 24;41(3):605-16. Upadhayay N, Guragain S, Khadka P. Linguistic Interference in reading Stroop card. Kathmandu University Medical Journal. 2022 Jun 30;20(2):178-82. Van Heuven WJ, Conklin K, Coderre EL, Guo T, Dijkstra T. The influence of cross-language similarity on within-and between-language Stroop effects in trilinguals. Frontiers in psychology. 2011 Dec 13;2:374. Hu Y, Kang X, Mao R. The languages impact on the stroop effect: Comparison in chinese and english. In2021 4th International Conference on Humanities Education and Social Sciences (ICHESS 2021) 2021 Dec 24 (pp. 929-933). Atlantis Press. Domingues‐Montanari S. Clinical and psychological effects of excessive screen time on children. Journal of paediatrics and child health. 2017 Apr;53(4):333-8. Vaidyanathan S, Manohar H, Chandrasekaran V, Kandasamy P. Screen time exposure in preschool children with ADHD: A cross-sectional exploratory study from South India. Indian Journal of Psychological Medicine. 2021 Mar;43(2):125-9. Sharma P, Budhathoki CB, Maharjan RK, Singh JK. Nutritional status and psychosocial stimulation associated with cognitive development in preschool children: A cross-sectional study at Western Terai, Nepal. PLoS One. 2023 Mar 13;18(3):e0280032. Ranabhat C, Kim CB, Park MB, Kim CS, Freidoony L. Determinants of body mass index and intelligence quotient of elementary school children in mountain area of Nepal: an explorative study. Children. 2016 Feb 3;3(1):3. Mishra SR, Joshi B, Poudyal Y, Adhikari B. Epistemic indebtedness: do we owe to epistemic enterprises?. Journal of Global Health Economics and Policy. 2022 Jul 10;2:e2022012. Additional Declarations No competing interests reported. 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In an academic context, the ability to concentrate on the instructional materials is critical for knowledge acquisition and retention.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The attentional abilities are developed significantly in early ages of life and are foundational for goal-directed learning. According to Posner\u0026rsquo;s attentional network theory,\u003csup\u003e2\u003c/sup\u003e the key components of attention in humans include three interconnected subsystems: the alerting network, which maintains sustained attention, required for continuing long term vigilance, the orienting network responsible for the selective attention to concentrate in a specific stimulus enabling students to focus on study material, and executive attention network allowing goal directed behaviors and conflict management. These attentional activities are primarily governed by the prefrontal cortex and associated regions of the brain and are recognized as crucial determinants of academic success.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Impairment in these subsystems, including attention deficit and poor inhibitory control, can impair information acquisition, memory processing, and consolidation, leading to disruptions in academic success.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAmong different systems, sustained attention is found to enable long-term engagement of students with lessons, while selective attention enables the purposeful selection of specific information in the face of classroom distractions, and executive attention helps in the execution of complex tasks in the context of competing cognitive demands.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Studies have demonstrated that students with better attention performance are better in core learning fields like language, arts, and mathematics.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e For example, Rabiner et al. (2016) observed that students of first grade with attention problems had poorer growth in mathematics and reading abilities compared to the other students without such problems.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Similarly, in a study including 700 school students, Gallen et al. found that attention was positively related to the performances on reading comprehension and math fluency as well as overall educational measures like statewide standardized test scores.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In a Meta-analysis including a review of 21 studies (n\u0026thinsp;=\u0026thinsp;7,947), it was reported that executive function (of which attention is a core component) had a moderately significant relationship with academic achievement, especially mathematics. Collectively, these findings show that attentional abilities have a direct correlation with academic outcomes in school students.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Among various neurocognitive performance tests measuring these abilities, the Stroop Color and Word Test (SCWT) and Trail Making Test (TMT) are widely used due to their validity and reliability.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e The SCWT is extensively used to evaluate the selective attention and inhibitory control by measuring the cognitive interference that occurs when the participants name the ink color of the words that denote conflicting colors.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Likewise, TMT part A measures the visual scanning, processing speed, and sustained attention, whereas part B assesses the executive attention and executive functions, including cognitive flexibility, inhibitory control, and working memory.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e These neurocognitive tests are particularly useful in school students, as they help identify attention deficits in early ages, which may impact the learning abilities of children.\u003c/p\u003e \u003cp\u003eIn South Asian contexts, including Nepal, various confounding factors such as study pressure and lack of resources, nutritional issues, and digital distractions can also moderate the relation between attention and learning. For example, Nepal\u0026rsquo;s academic environment often places a high value on academic achievement, and underperformers may be treated unfavorably and may go through harsh punishment at home and school, and even the children with attention problems may be stigmatized or punished for poor performance.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Mental health research in Nepal indicates that the prevalence of attention deficit hyperactivity disorder (ADHD) is common: one hospital-based study found this prevalence to be 11.7% among school children, suggesting that Nepali students struggle with attention deficit and associated executive function impairments.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Apart from that, other regional factors such as crowded classrooms, limited special education, and cultural attitudes towards discipline are the things to be considered while studying the correlation between attention and academic achievements in Nepali School children. Moreover, nutritional deficiency among the children with low BMI.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and high engagement in mobile phones and screens exacerbating distractions, needs localized investigations.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Modifiable lifestyle factors have been seen to positively impact attentional abilities. For instance, researchers have observed that children engaged in leisure-time physical activity and reading for at least one hour on weekdays demonstrate significantly better attention scores, independent of socioeconomic factors.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Furthermore, cognitive training, including memory exercises and computerized games found to have a positive impact on attentional and academic performances.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Similarly, studies have shown that regular physical exercise and mindfulness meditation training boost sustained and selective attention in children.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Despite the evidence from the Western and some Asian contexts linking attention with academics, there remains a gap in the South Asian population, and even the complete absence of studies focused on this topic in Nepal. This study, conducted in Bhairahawa, Nepal, among the school children of 6 to 10 grades, tries to find these voids by investigating the relationship between attention as assessed through SCWT and TMT, and academic performances, incorporating modifiable factors like screen use and undernutrition as measured by BMI. We hypothesize a positive correlation between SCWT scores (indicating better selective and executive attention) and a negative correlation of TMT score (increase in time reflecting poorer sustained and executive attention) with marks secured in the exam. This research contributes novel data on attention academic links in the Nepali context, highlights modifiable risk factors, and advocates the target strategies to mitigate better learning.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study design and setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional, observational study was conducted among school-going children of Bhairahawa, Lumbini province, Nepal. It was conducted in two private and one government secondary school from October 2024 to February 2025 following the ethical approval from the Institutional Review Committee of Universal College of Medical Sciences (UCMS), Bhairahawa (UCMS/IRC/098/24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Sample size calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample size was calculated using Fisher\u0026apos;s z transformation to find the correlation between attentional test performance and academic scores. Following formula was used:\u003c/p\u003e\n\u003cp\u003en = ((Z\u0026alpha;/2 + Z\u0026beta;) / (0.5 \u0026times; ln [(1 + r) / (1 \u0026minus; r)]))\u0026sup2; + 3\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003er= expected correlation\u003c/p\u003e\n\u003cp\u003eZ\u0026alpha;/2=standard normal value for two tailed \u0026alpha;\u003c/p\u003e\n\u003cp\u003eZ\u0026beta;= normal deviate corresponding to desired power\u003c/p\u003e\n\u003cp\u003eIn our study, we assumed a conservative expected correlation of r=0.205 from a similar previous study,\u003csup\u003e22\u003c/sup\u003e two-tailed \u0026alpha;=0.05, and statistical power, 80% (Z\u0026beta;=0.84) and Z\u0026alpha;/2=1.96. While putting the values in the formula, the minimum required sample size was approximately n=184 participants. To allow for clustering by school, planned multivariate analyses, and potential missing data, the sample was increased, and a total of 377 students were finally included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Participant selection and sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participants of the study comprised male and female students in grades 6 to 10. A stratified random sampling technique was employed to ensure the representativeness. For selection of schools, we obtained a list of schools from the district education office and applied simple random (lottery method), selecting three secondary schools among a total of 34 schools of Bhairahawa. Within each selected school, students were stratified by grade, and the required number of participants from each grade was determined proportionally to the total enrollment size. In grades nine and ten in one of these two private schools, two sections were present, so data collection was done from both sections. In each class, 30 students were randomly chosen using a lottery method to participate in the study. After selection, information sheets and assent forms were distributed to selected students, along with the parental consent forms. Data collection was conducted on the following day only from those participants who met the following inclusion and exclusion criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eStudents enrolled in 6 to 10 grades.\u003c/li\u003e\n \u003cli\u003eThose who provided signed parental/guardian consent and provided their own assent.\u003c/li\u003e\n \u003cli\u003eStudents present on the scheduled day of data collection.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eKnown cases of psychiatric, neurological, and developmental disorders\u003c/li\u003e\n \u003cli\u003eVisual or motor impairments that would prevent valid completion of TMTs.\u003c/li\u003e\n \u003cli\u003eColor deficiency as identified by the Ishihara color plate (for SCWT suitability)\u003c/li\u003e\n \u003cli\u003eNot willing to take part in the study\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA case proforma was used to record sociodemographic data, including, age, sex, dietary habits, and medical history. Height was measured with stadiometer and weight was taken by digital weighing machine, and BMI was calculated (kg/m\u003csup\u003e2\u003c/sup\u003e) from the obtained values. The BMI was classified as thinness (undernutrition), normal, overweight and obese as per the WHO classification for children.\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAcademic performance was assessed from the aggregate marks (%) from the most recent final school examination (the last academic year). Marks were obtained from the record section of the schools with permission. The overall percentage and subject-wise scores were recorded for the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStroop -Color Word Test (SCWT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStroop- Color Word Test was used as a tool for assessing Selective attention and inhibitory control. For the Stroop test, three subtasks were administered using the paper test format, and a timer of 45 seconds was set for each subtask. In the Word test, the participants were instructed to read as many words (W) as they could. In the Color test (C), the participants were instructed to name the color. In the Color/Word test, they were instructed to read the ink color while ignoring the written words. The number of correct responses per subtest was recorded. Interference Score (IG) was calculated using the formula IG = [CW-W*C/(W+C)]. The SCWT procedure and scoring technique follow the established descriptions \u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrail Making Test (TMT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSelective attention, sustained attention, and other executive functions\u003csup\u003e24\u003c/sup\u003e (which include interference control, cognitive flexibility, and working memory) were evaluated by the Trail Making Tests (TMT). First of all, the participants were given proper instructions regarding the examination. \u0026nbsp;In TMT: A, they were instructed to join the numbers from 1 to 25 on a piece of paper using a pencil, and the time taken was noted. Similarly, in TMT: B, the participants were instructed to join numbers and letters alternately as 1 A, 2 B, and so on up to 13. The time taken to complete the task was recorded for each part. The participants were not allowed to lift the pencil off the paper, and time penalties were noted as per standard administration and interpretations follow standard guidelines and normative references.\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Testing procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection was conducted in a quiet, well-lit room in the assembly halls of the schools during regular school hours. After verifying the consent/assent forms and collecting demographic and anthropometric data, tests were administered in the following order: Stroop test (W, C, CW), then TMT-A and TMT-B. Each participant completed the test individually under the supervision of the investigator in a one-to-one setting. All the investigators were trained in standardized instructions, error-handling rules, and timing procedures prior to the real investigation. A pilot study of 20 students was performed to verify comprehension for the Stroop test, standardize timing, scoring, and data recording. These samples were not included in the final data. Minor adjustments were made to the clarity of verbal instructions based on pilot feedback. All the raw data were double-checked by a second investigator for data entry accuracy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were entered into Microsoft Excel 2010 and analyzed using IBM SPSS Statistics 20.0. Descriptive statistics were used to summarize participant characteristics. Categorical variables were presented as frequencies and percentages, while continuous variables were expressed as mean \u0026plusmn; standard deviation (SD) with normality assessed via the Shapiro-Wilk test. Pearson correlation coefficients were computed between Stroop IG, Trail A, Trail B, academic subject scores, overall scores, BMI, and class. Multiple linear regression models predicted cognitive outcomes (Stroop IG, Trail A, Trail B times) from academic scores (Math, English, Science, Nepali, and overall percentage). All analyses were two-tailed, with statistical significance set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOut of a total of 764 students, 377 students from grades 6 to 10 were included in the study. Table 1 describes the general characteristics of the students participated in the study. Sex ratio was 0.68, favouring males. More than half of the students participated in grades 9 and 10. Most of the children were non-vegetarian, and the majority of them had no significant medical history. Among those having a history, hypothyroidism, vertigo, appendicitis, menstrual disturbance, jaundice, myopia, and seasonal allergy were common. More than half (56%) of children report using some device (mostly a smartphone) at bedtime, and its use at meal time was also common in over a quarter of them. About a fifth (18.57%) of the participating children were under the category of underweight, i.e., their BMI was lower than 2 SD below the median BMI for their age, and about 10% were overweight or obese (BMI higher than 1 SD above the median BMI for their age) as per the WHO classification.\u003c/p\u003e\n\u003cp\u003eTable 1. General characteristics of participants and their frequency (n=377)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"428\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 100%;\"\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 valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e40.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e59.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e15.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e13.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e17.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e28.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e25.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eVegetarian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e13.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eNon-vegetarian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e86.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical History\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e95.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eSome history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBedtime Screen use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eNo screen at bedtime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e44.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003escreen used at bedtime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e56.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScreen time per day for entertainment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003e\u0026lt;1 hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e26.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003e1-2 hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e43.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003e2-3 hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e15.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003e3-4 hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e8.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003e\u0026gt;4 hour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e6.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScreen use at meal time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e35.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eRarely\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e10.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eSometimes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e28.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eOften\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e5.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eAlways\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e19.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody Mass index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e18.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e71.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e7.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.729%;\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.7009%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.5701%;\"\u003e\n \u003cp\u003e2.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 shows the descriptive values of anthropometric and cognitive function. The mean age of participants is 14.42 years (SD = 1.65), with a range of 8-18 years. The average height, weight, and BMI indicate that most students fall within normal ranges.\u003c/p\u003e\n\u003cp\u003eThe Stroop test results show moderate levels of performance in word reading (82.3 \u0026plusmn; 17.86 words read in 45 sec), colour naming (56.17 \u0026plusmn; 11.52 colours named in 45 sec), and colour-word interference (34.3 \u0026plusmn; 8.2 colours named in 45 sec in the incongruent list). The IG score suggests high variability across tasks; about half (175, 46.42%) of the students got a negative IG score as per the calculation, making the total mean score just 1.1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Descriptive characteristics of participants (n=377)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"506\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.336%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1225%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.5415%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.336%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1225%;\"\u003e\n \u003cp\u003e14.42 \u0026plusmn; 1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.5415%;\"\u003e\n \u003cp\u003e8 - 18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.336%;\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1225%;\"\u003e\n \u003cp\u003e158.39 \u0026plusmn; 10.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.5415%;\"\u003e\n \u003cp\u003e127 - 181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.336%;\"\u003e\n \u003cp\u003eWeight (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1225%;\"\u003e\n \u003cp\u003e46.09 \u0026plusmn; 9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.5415%;\"\u003e\n \u003cp\u003e21.9 - 83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.336%;\"\u003e\n \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1225%;\"\u003e\n \u003cp\u003e18.3 \u0026plusmn; 3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.5415%;\"\u003e\n \u003cp\u003e11.6 - 32.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.336%;\"\u003e\n \u003cp\u003eStroop test Word (number)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1225%;\"\u003e\n \u003cp\u003e82.3 \u0026plusmn; 17.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.5415%;\"\u003e\n \u003cp\u003e15 - 128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.336%;\"\u003e\n \u003cp\u003eStroop test Colour (number)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1225%;\"\u003e\n \u003cp\u003e56.17 \u0026plusmn; 11.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.5415%;\"\u003e\n \u003cp\u003e14 - 96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.336%;\"\u003e\n \u003cp\u003eStroop test Colour-word (number)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1225%;\"\u003e\n \u003cp\u003e34.3 \u0026plusmn; 8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.5415%;\"\u003e\n \u003cp\u003e10 - 61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.336%;\"\u003e\n \u003cp\u003eStroop Test IG socre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1225%;\"\u003e\n \u003cp\u003e1.1 \u0026plusmn; 6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.5415%;\"\u003e\n \u003cp\u003e-27.48 - 25.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.336%;\"\u003e\n \u003cp\u003eTrail A (second)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1225%;\"\u003e\n \u003cp\u003e37.11 \u0026plusmn; 12.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.5415%;\"\u003e\n \u003cp\u003e6.14 - 99.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.336%;\"\u003e\n \u003cp\u003eTrail B (second)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1225%;\"\u003e\n \u003cp\u003e80.86 \u0026plusmn; 31.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.5415%;\"\u003e\n \u003cp\u003e22.8 - 285.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBivariate correlation between different numerical parameters is shown in Table 3. It shows that the Trail A and Trail B tests are significantly negatively correlated with all the academic scores (p\u0026lt;0.001), signifying that the longer the duration of the Trail tests, the poorer the academic performance. But the Stroop IG score shows no significant relation with academic scores (p\u0026gt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Bivariate Pearson correlation between different parameters of cognitive test scores, academic scores, age, BMI and class (grade) in school. For each parameter, first row shows correlation coefficient, and second gives corresponding p value for the coefficient.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"650\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003eTrail A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eTrail B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003eMath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eEnglish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eScience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003eNepali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eStroop IG score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eTrail A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.440*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-.301*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-.287*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-.243*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-.298*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-.187*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.176*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.166*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eTrail B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-.198*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-.179*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-.129*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-.137*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-.110*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-.177*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eMath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e.799*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e.831*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.834*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e.817*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.177*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.155*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eEnglish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e.845*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.808*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e.748*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.152*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.104*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.136*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eScience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.832*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e.794*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.107*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.105*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNepali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e.748*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.109*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.121*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.116*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003cp\u003escore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.110*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e.150*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.632*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.148*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 650px;\"\u003e\n \u003cp\u003e* Correlations significant at 95% confidence.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo examine the relationships between academic subject scores and cognitive test performance, we conducted a series of linear regression analyses (Table 4). We used separate models to predict each cognitive test score (Stroop IG, Trail A, and Trail B as dependent variables) from the academic subject scores (Math, English, Science, Nepali, and overall percent score, as independent variables). We assumed that the data met the standard assumptions of linear regression, including linearity, independence, homoscedasticity, normality of residuals, and little to no multicollinearity.\u003c/p\u003e\n\u003cp\u003eFor the Stroop IG score, only the Nepali score was a significant predictor (\u0026beta; = -0.23, p = 0.030), with a small effect size (R\u0026sup2; = 0.02). The model was not significant (F = 1.28, p = 0.271). \u003cstrong\u003eFor Trail A as the dependent variable,\u003c/strong\u003e Math (\u0026beta; = -0.30, p = 0.007) and overall academic percent score (\u0026beta; = 0.22, p = 0.016) were significant predictors, with a moderate effect size (R\u0026sup2; = 0.12). The model was significant (F = 10.12, p \u0026lt; 0.001). Similarly, for Trail B, Math (\u0026beta; = -0.34, p = 0.003) was a significant predictor, with a small to moderate effect size (R\u0026sup2; = 0.06). The model was significant (F = 4.42, p = 0.001). Specifically, for every one-unit increase in Mathematics score, there is a corresponding 0.30-point decrease on the Trail A test (p = .007) and a 0.34-point decrease on the Trail B test (p = .003). The other subject scores did not show significant relationships with any cognitive performance.\u003c/p\u003e\n\u003cp\u003eTable 4. Multiple linear regression analysis of three cognitive scores in relation to the students\u0026rsquo; academic performance (exam scores)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 642px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroop Test IG score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 221px;\"\u003e\n \u003cp\u003eR Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eAdjusted R Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 115px;\"\u003e\n \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 221px;\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 115px;\"\u003e\n \u003cp\u003e6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 221px;\"\u003e\n \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eStandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eMath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eEnglish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eScience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eNepali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e-.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.030*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eTotal Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e-.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 642px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrail A score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 221px;\"\u003e\n \u003cp\u003eR Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eAdjusted R Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 115px;\"\u003e\n \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 221px;\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 115px;\"\u003e\n \u003cp\u003e11.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 221px;\"\u003e\n \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eStandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e44.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e19.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eMath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e-.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eEnglish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e-.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eScience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eNepali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e-.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eTotal Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 642px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrail B Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 221px;\"\u003e\n \u003cp\u003eR Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eAdjusted R square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 115px;\"\u003e\n \u003cp\u003eStd. Error of the estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 221px;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 115px;\"\u003e\n \u003cp\u003e30.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 221px;\"\u003e\n \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eStandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e88.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e14.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eMath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e-.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eEnglish\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e-.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eScience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eNepali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eTotal Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 642px;\"\u003e\n \u003cp\u003eFor each model of cognitive score, independent variables are academic scores of Math, English, Science, Nepali and total score.\u003c/p\u003e\n \u003cp\u003e* Significant at 95% confidence.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThese regression findings suggest that academic subject scores are associated with cognitive test performance, especially in Trail A and Trail B, but not of Stroop IG score. However, the effect sizes are small, and the models do not explain a large proportion of the variance in the cognitive test scores.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional study, 377 schoolchildren of grades 6-10 from Bhairahawa, Nepal, were assessed to find out the relationship between attentional abilities and their academic performance. The neurocognitive assessments of attention were done by the Stroop Color Word Test (SCWT) and the Trail Making Test (TMT), and academic performance was assessed through the aggregate marks (%) from the most recent final school examination of the last academic year. The result showed that there was a significant negative correlation between the time taken to complete TMT-A and TMT-B and academic performance. This illustrates that slower performance on these tests, reflecting poorer sustained attention, processing speed, and executive attention, was linked to lower grades in the examination. In our findings, the scores secured in mathematics were the strong predictors in regression models for both TMT-A (\u0026beta; = -0.30, p = 0.007) and TMT-B (\u0026beta; = -0.34, p = 0.003), with overall academic percentage also significant for TMT-A (\u0026beta; = 0.22, p = 0.016). Whereas, there was no significant association between interference measure (IG Score) and academic performance, apart from a weak negative relationship with Nepali scores in regression (\u0026beta; = -0.23, p = 0.030), though the model was not significant. Moreover, the use of screens during bedtime was prevalent (56%) among students, and was more common among the boys, while almost one fifths of the children were underweight, highlighting the potential confounding factors influencing cognition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings of the present study are in alignment with the previous studies, which have established the association between attentional abilities and academic performance. The inverse relationship between TMT and academic performance is consistent with other studies\u003csup\u003e26-29\u003c/sup\u003e showing that longer TMT completion times predict poorer academic performance in children, as TMT taps into executive functions necessary for problem-solving skills in subjects like mathematics. \u0026nbsp;In a large school-based study of children, Gallen et al. found that sustained attention performance was positively linked with both math fluency and reading comprehension, along with standardized test scores.\u003csup\u003e30\u003c/sup\u003e Similarly, in a meta-analysis including 21 studies (n=8000) in 6-12-year-old students, the researchers found that there is a moderate pooled correlation between general executive function and academic performance, with a slightly stronger effect on mathematics than language. The results of the present study in the Nepali context, indicating math scores as the most predictive of TMT performance, are consistent with these findings, supporting the notion that better attentional control enhances mathematical problem-solving ability. However, the present study found no significant association between Stroop test\u0026nbsp;Interference Score (IG) and academic outcomes contrasting with some previous studies. For instance, the results reported by Dvorak (2024)\u003csup\u003e31\u003c/sup\u003e showed a weak but statistically significant correlation between the Stroop Effect and academic performance in the University of Sweden. Similarly, in another Iranian study, a statistically significant relationship was found between the Stroop test and academic achievement\u003csup\u003e32\u003c/sup\u003e suggesting that the cognitive capacity of inhibitory control as measured by this test may be a predictive factor for academic achievements. Unlike these studies, our sample from Nepali school children showed no relationship between the Stroop test and academic performance. There could be several reasons behind this outcome. First, it could be due to the developmental differences, as many prior studies included older adolescents or university students whose inhibitory control and interference resolution are more mature, whereas our participants consisted of younger schoolchildren whose neurocognitive pathways for overcoming the interferences in the Stroop effect are still developing. Second, the issues regarding the task performances and language factors may also play a role, as our Stroop test was administered in English, and even though students were familiar with English as a medium of instruction, it remains a non-native language for Nepali students, which could lead to reading proficiency confounds. Finally, the contextual factors, including teaching methods, large class size, syllabus focus, emphasis on rote learning education, and differences in examination patterns, could produce the impact. Indeed, other studies have shown an inconsistent relationship between SCWT and academic outcomes, reflecting that Stroop performance can be influenced by reading ability and motivation apart from pure inhibitory control.\u003csup\u003e33,34\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eTheoretically, our results can be interpreted through Posner\u0026rsquo;s framework of attentional networks,2 where alerting (TMT-A) sustains the attention on a desired task, orienting (SCWT Color) selects relevant inputs, and executive attention (TMT-B and SCWT IG) resolves the interferences during the complex learning process. \u0026nbsp;So, TMT and SCWT recruit partly overlapping but distinct attentional mechanisms due to different neuronal circuits. While exploring in detail, TMT-A mainly measures visual processing, visuomotor scanning, and processing speed, which are the neuronal functions of dorsal attention and occipito-parietal sensorimotor pathways; TMT-B needs an alternating task that substantially engages dorsolateral prefrontal cortex, anterior cingulate, and broader fronto-parietal control systems that also require the maintenance of sequence information via working memory.\u003csup\u003e35\u003c/sup\u003e Slower TMT times reflect the compound phenomenon, including decreased processing speed, limited maintenance of working memory, and less efficient fronto-\u0026nbsp;parietal coordination. Large pediatric normative studies show a clear age-related decrease in TMT-A and TMT-B completion times.\u003csup\u003e36-38\u003c/sup\u003e These findings are quite consistent with the findings of our studies, as it shows a statistically significant negative correlation of age with both the trail making test times. This demonstrates that as children grow older, maturation of fronto-parietal networks supports faster processing speed, improved sustained and selective attention, and more efficient cognitive control, leading to shorter completion times. Furthermore, while comparing with the previous studies, it was revealed that typical TMT-A and TMT-B times for mid adolescents are often somewhat lower than the mean of our sample of both the Trail tests.\u003csup\u003e35,37\u003c/sup\u003e This difference is attributable to regional, educational, nutritional or testing familiarity factors.\u003c/p\u003e\n\u003cp\u003eIn contrast, the Stroop Color Word Test IG score is a classical measure of cognitive interference and engages a neuronal network of conflict monitoring and resolution. To execute this mechanism, the anterior cingulate cortex signals response conflict, and the lateral prefrontal region (particularly dorsolateral and ventrolateral PFC) implements the top-down suppression of the automatic tendency to read the word rather than name the ink color.\u003csup\u003e38\u003c/sup\u003e Moreover, recent neuroimaging evidence has identified a cross-hemispheric, excitatory-inhibitory loop between the left lateral PFC and the right cerebellum. During Stroop interference, the PFC appears to enhance the activity of the cerebellum, whereas the cerebellum exerts inhibitory feedback on the PFC, maintaining a dynamic balance leading to refinement in conflict resolution and timing of response suppression.\u003csup\u003e39\u003c/sup\u003e Developmental studies have further postulated that ACC and prefrontal cortex activation are responsible for these interference resolution increases with age, reflecting maturation of top-down control systems through childhood to adolescence and reaching to its optimum level in adulthood.\u003csup\u003e40\u003c/sup\u003e In the present study, the mean value of IG=1.1 \u0026plusmn; 6.83 is small and close to zero, indicating a low average interference score, but the variability was very high, ranging from -27.48 to 25.33, indicating that there is partial maturation of interference control in early adolescence. This can also be due to measurement compression because the test was administered in English, which remains a non-native language as discussed earlier, or may be because the difference between the Color-word and Word condition may have been reduced in our sample making the interference score less sensitive to pure inhibitory control.\u003csup\u003e41\u003c/sup\u003e Indeed, previous research conducted in the Nepali population also reveals that administration format and language affect Stroop measures\u003csup\u003e42\u003c/sup\u003e and several validation efforts recommend administering the task in students\u0026rsquo; native language.\u003csup\u003e43,44\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, we also explored the potential contribution of screen exposure and nutritional status as measured by BMI to attention and academics. Over half of our participants reported bedtime device use (with boys more affected), which is consistent with the global scenario and can be one of the factors disrupting sleep and attention, and indirectly impacting academics.\u003csup\u003e45\u003c/sup\u003e In the South Asian context, the penetration of smartphones is very high, and a study conducted in India has shown the clear link between excessive screen time and reduced executive function, decreased academic performance.\u003csup\u003e46\u003c/sup\u003e Moreover, it was observed that almost one-fifth of students were underweight, which may have further complicated the true relationship between attention and academic performance. Nutrition status has been linked to cognitive development among the children of Nepal, with deficient nutrition being associated with lower scores in cognitive examinations in children, as highlighted in Rupandehi (a district in which our study area falls), Nepal.\u003csup\u003e47\u003c/sup\u003e Similarly, another study in Nepal has also highlighted that malnutrition prevalent in Nepali children due to poor socioeconomic status may have some impact on cognitive development.\u003csup\u003e48\u003c/sup\u003e Taken together, these findings suggest that high screen exposure and BMI are potentially modifiable factors that could impair attention and academic performance. Thus, at the level of school, interventions targeting healthy nutrition and enhancing awareness regarding limiting screen time may help enhance attention and, hence, the academic outcomes to some extent.\u003c/p\u003e\n\u003cp\u003eThe strength of the study includes a large, multi-school sample and standardized testing; key limitations are cross-sectional design and English Stroop administration. Future work should use longitudinal designs, native language Stroop variants, and broader cognitive batteries. Producing such locally grounded evidence also helps strengthen Nepal\u0026rsquo;s research voice and reduce epistemic dependency.\u003csup\u003e49\u003c/sup\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the attentional abilities and their links to academic performance in Nepali schoolchildren, finding a significant negative correlation between Trail Making Test (TMT) times and grades in the exam, particularly in mathematics, reflecting that processing speed, sustained attention, and executive attention were consistently associated with higher academic scores. There was no robust association between Stroop interference scores and academic achievements, likely because of developmental and language influences. The findings highlight the role of attention in education and suggest that schools might boost learning by supporting attention (e.g., brief attention training, structured physical exercise, and demotivating screen use).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eACC\u003c/strong\u003e- Anterior cingulate cortex\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e- Body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCW\u003c/strong\u003e- Color-Word condition of Stroop test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDLPFC\u003c/strong\u003e- Dorsolateral Prefrontal Cortex\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEF\u003c/strong\u003e- Executive functions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIG\u003c/strong\u003e- interference score (Stroop Color-Word interference)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePFC\u003c/strong\u003e- Prefrontal Cortex\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCWT\u003c/strong\u003e- Stroop Color-Word Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e- Standard deviation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTMT-A\u003c/strong\u003e-Trail Making Test Part A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTMT-B\u003c/strong\u003e- Trail making test Part B\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and Consent to Participate\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the Institutional Review Committee (IRC) of Universal College of Medical Sciences, Bhairahawa Nepal (Reference No: UCMS/IRC/098/24). Permission to conduct the study was obtained from all participating school. Written informed consent was obtained from parents/guardians of all participants students, and written assent was obtained from the students themselves prior to data collection. All the procedures were conducted in accordance with the Declaration of Helsinki and relevant national guidelines.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available \u0026nbsp;but will be available from the corresponding author (B.J.) on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB.J. designed the research protocol, coordinated data collection, performed the primary data analysis, and manuscript writing. J.P.J. performed overall data analysis, supervised the overall methodology, guided statistical analysis, and provided critical revisions to the manuscript. Y.Y. and B.K.A. assisted in data acquisition, data management and preliminary analysis. S.A.M., A.T., A.K., and L.S. contributed to field coordination, data collection, quality assurance and manuscript editing. All authors reviewed, contributed to, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the participants from all the three schools who took part in our study in an enthusiastic way. We would also like to thank administrations of all the schools for making smooth arrangements for the data collection team.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGallen, Courtney L., et al. \u0026quot;Contribution of sustained attention abilities to real-world academic skills in children.\u0026quot; Scientific reports 13.1 (2023): 2673.\u003c/li\u003e\n \u003cli\u003ePosner MI, Rothbart MK. Research on attention networks as a model for the integration of psychological science. Annu. Rev. Psychol.. 2007 Jan 10;58(1):1-23.\u003c/li\u003e\n \u003cli\u003eBunge SA, Dudukovic NM, Thomason ME, Vaidya CJ, Gabrieli JD. Immature frontal lobe contributions to cognitive control in children: evidence from fMRI. Neuron. 2002 Jan 17;33(2):301-11.\u003c/li\u003e\n \u003cli\u003eAreces D, Dockrell J, Garcia T, Gonzalez-Castro P, Rodriguez C. 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BMI-for-age (5-19 years) [Internet]. Geneva: World Health Organization; [cited 2025 Nov 12]. Available from: https://www.who.int/tools/growth-reference-data-for-5to19-years/indicators/bmi-for-age.\u003c/li\u003e\n \u003cli\u003eDiamond A. Executive functions. Annual review of psychology. 2013 Jan 3;64(1):135-68.\u003c/li\u003e\n \u003cli\u003eTombaugh TN. Trail Making Test A and B: normative data stratified by age and education. Archives of clinical neuropsychology. 2004 Mar 1;19(2):203-14.\u003c/li\u003e\n \u003cli\u003eWarneke K, Oraže M, Pl\u0026ouml;schberger G, Herbsleb M, Afonso J, Wallot S. When Testing Becomes Learning\u0026mdash;Underscoring the Relevance of Habituation to Improve Internal Validity of Common Neurocognitive Tests. European Journal of Neuroscience. 2025 Apr;61(8):e70117.\u003c/li\u003e\n \u003cli\u003eAr\u0026aacute;n Filippetti V, Gutierrez M. Interpreting the direct-and derived-Trail Making Test scores in Argentinian children: regression-based norms, convergent validity, test-retest reliability, and practice effects. The Clinical Neuropsychologist. 2025 Aug 18;39(6):1696-721.\u003c/li\u003e\n \u003cli\u003eOksuz S, Depreli O, Ozdil A, Iyigun G, Angın E, Bilgin BF, Karaman A. Comparison of Cognitive Functions, Sleep Quality, Emotional State, Balance and Physical Activity Levels of University Students According to Academic Success. Internet Journal of Allied Health Sciences and Practice. 2025;23(2):2.\u003c/li\u003e\n \u003cli\u003eWaggestad TH, Kirsebom BE, Strobel C, Wallin A, Eckerstr\u0026ouml;m M, Fladby T, Egeland J. Improving validity of the trail making test with alphabet support. Frontiers in Psychology. 2023 Jul 27;14:1227578.\u003c/li\u003e\n \u003cli\u003eGallen CL, Schaerlaeken S, Younger JW, Anguera JA, Gazzaley A. Contribution of sustained attention abilities to real-world academic skills in children. Scientific reports. 2023 Feb 15;13(1):2673.\u003c/li\u003e\n \u003cli\u003eDvorak M. Inhibitory control and academic achievement\u0026ndash;a study of the relationship between Stroop Effect and university students\u0026rsquo; academic performance. BMC psychology. 2024 Sep 27;12(1):498.\u003c/li\u003e\n \u003cli\u003eSoltani Nezhad M, Delroba M. Investigating the relationship between students\u0026rsquo; executive functions, emotion regulation, and academic achievement. Discover Psychology. 2024 Sep 18;4(1):120.\u003c/li\u003e\n \u003cli\u003eProtopapas A, Archonti A, Skaloumbakas C. Reading ability is negatively related to Stroop interference. Cognitive Psychology. 2007 May 1;54(3):251-82.\u003c/li\u003e\n \u003cli\u003eVarjacic A, Mantini D, Demeyere N, Gillebert CR. Neural signatures of Trail Making Test performance: Evidence from lesion-mapping and neuroimaging studies. Neuropsychologia. 2018 Jul 1;115:78-87.\u003c/li\u003e\n \u003cli\u003eArango-Lasprilla JC, Rivera D, Aguayo A, Rodr\u0026iacute;guez W, Garza MT, Saracho CP, Rodr\u0026iacute;guez-Agudelo Y, Aliaga A, Weiler G, Luna M, Longoni M. Trail making test: Normative data for the Latin American Spanish speaking adult population. NeuroRehabilitation. 2015 Nov 26;37(4):639-61.\u003c/li\u003e\n \u003cli\u003eTombaugh TN. Trail Making Test A and B: normative data stratified by age and education. Archives of clinical neuropsychology. 2004 Mar 1;19(2):203-14.\u003c/li\u003e\n \u003cli\u003eG\u0026uuml;nd\u0026uuml;z H, G\u0026uuml;nd\u0026uuml;z GB, G\u0026Uuml;LVEREN H, TAVAT BC. Norm determination study of trail making test, enhanced cued recall test and clock drawing test for Turkish sample between 6-18 years of age. Archives of Neuropsychiatry. 2021 Aug 2;58(4):314.\u003c/li\u003e\n \u003cli\u003ePeterson BS, Skudlarski P, Gatenby JC, Zhang H, Anderson AW, Gore JC. 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Stroop color-word interference test: Normative data for Spanish-speaking pediatric population. NeuroRehabilitation. 2017 Oct 24;41(3):605-16.\u003c/li\u003e\n \u003cli\u003eUpadhayay N, Guragain S, Khadka P. Linguistic Interference in reading Stroop card. Kathmandu University Medical Journal. 2022 Jun 30;20(2):178-82.\u003c/li\u003e\n \u003cli\u003eVan Heuven WJ, Conklin K, Coderre EL, Guo T, Dijkstra T. The influence of cross-language similarity on within-and between-language Stroop effects in trilinguals. Frontiers in psychology. 2011 Dec 13;2:374.\u003c/li\u003e\n \u003cli\u003eHu Y, Kang X, Mao R. The languages impact on the stroop effect: Comparison in chinese and english. In2021 4th International Conference on Humanities Education and Social Sciences (ICHESS 2021) 2021 Dec 24 (pp. 929-933). Atlantis Press.\u003c/li\u003e\n \u003cli\u003eDomingues‐Montanari S. Clinical and psychological effects of excessive screen time on children. Journal of paediatrics and child health. 2017 Apr;53(4):333-8.\u003c/li\u003e\n \u003cli\u003eVaidyanathan S, Manohar H, Chandrasekaran V, Kandasamy P. Screen time exposure in preschool children with ADHD: A cross-sectional exploratory study from South India. Indian Journal of Psychological Medicine. 2021 Mar;43(2):125-9.\u003c/li\u003e\n \u003cli\u003eSharma P, Budhathoki CB, Maharjan RK, Singh JK. Nutritional status and psychosocial stimulation associated with cognitive development in preschool children: A cross-sectional study at Western Terai, Nepal. PLoS One. 2023 Mar 13;18(3):e0280032.\u003c/li\u003e\n \u003cli\u003eRanabhat C, Kim CB, Park MB, Kim CS, Freidoony L. Determinants of body mass index and intelligence quotient of elementary school children in mountain area of Nepal: an explorative study. Children. 2016 Feb 3;3(1):3.\u003c/li\u003e\n \u003cli\u003eMishra SR, Joshi B, Poudyal Y, Adhikari B. Epistemic indebtedness: do we owe to epistemic enterprises?. Journal of Global Health Economics and Policy. 2022 Jul 10;2:e2022012.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"attention, Trail making test, Stroop, academic performance, schoolchildren, Nepal","lastPublishedDoi":"10.21203/rs.3.rs-8223950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8223950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAttention is a key cognitive factor in classroom learning, but the relationship between attentional abilities and academic performance remains understudied in South Asian countries like Nepal. This study aimed to assess attentional performance by the Stroop Color Word Test (SCWT) and Trail Making Test (TMT) and its relationship with academic performance among schoolchildren in Bhiarahawa, Nepal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this cross-sectional study, 377 students from grades 6 to 10 were included using stratified random sampling from three schools. Neurocognitive assessment of attention was done by SCWT (Word, Color, Color-Word), with calculated Interference score (IG) and TMT-A and TMT-B. Academic performance was taken from the official school mark-sheet (overall percentage and subject scores). Sociodemographic data, anthropometrics, and screen use habits were collected. Associations among the variables were evaluated with Pearson correlations and multiple linear regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe Stroop test score in 45 seconds was Word 82.3 ± 17.86, Color 56.17 ± 11.52, Color-Word 34.3 ± 8.2, with a calculated interference score (IG) of 1.1 ± 6.8, whereas the time for TMT-A was 37.1 sec (±12.6); TMT-B was 80.9 sec (±31.4). There was a significant inverse relationship between TMT A and TMT-B completion times and overall academic percentage and subject scores, signifying that faster TMT performance corresponds to better academic performance, especially in mathematics, as confirmed by regression (β = -0.30 for TMT-A and β=-0.34 for TMT-B). In contrast, the SCWT IG score had no significant association.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIn this study, TMTs were the robust correlates of the academic outcomes, particularly mathematics, whereas the Stroop test was not a significant predictor of academic achievement. These findings emphasize that school-based interventions for enhancing the attentional abilities of students can greatly support learning. In the future, longitudinal studies using culturally adapted measures are recommended.\u003c/p\u003e","manuscriptTitle":"Neurocognitive Assessment of Attention and Its Relationship With Academic Performance Among Schoolchildren","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 14:04:37","doi":"10.21203/rs.3.rs-8223950/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-09T01:42:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T05:58:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T10:47:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T18:30:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263015067096352326067708974057409196448","date":"2026-04-21T00:42:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T10:58:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235235232632497233983071907823413824930","date":"2026-04-18T05:08:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61726419540500510122385610076645228213","date":"2026-04-17T12:22:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277701737555396192616567354692072898310","date":"2026-04-17T10:01:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230419602720065937773071011122440912535","date":"2026-04-15T14:59:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163334721686725337331184402168253987466","date":"2026-04-07T13:46:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T12:42:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-03T00:13:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-02T15:06:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-02T05:34:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-12-02T05:26:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03ebaaea-4400-4447-ac23-594f52411dfc","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-09T01:42:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T05:58:21+00:00","index":77,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T01:54:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 14:04:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8223950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8223950","identity":"rs-8223950","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}