Effect of a collaborative learning–based nutrition education intervention on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria: a quasi-experimental study

Effect of a collaborative learning–based nutrition education intervention on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria: a quasi-experimental study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effect of a collaborative learning–based nutrition education intervention on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria: a quasi-experimental study Dare Damilola Adémiluyi, Ojo-Adalumo Ayobami Rhoda, Esther Danladi Olubiyi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8560558/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Discover Public Health → Version 1 posted 14 You are reading this latest preprint version Abstract Background Poor diet quality and inadequate nutrition knowledge during adolescence contribute to micronutrient deficiencies and increasing non-communicable disease (NCD) risk in low- and middle-income countries. Evidence remains limited on whether collaborative learning–based nutrition education improves objectively measured diet quality among adolescents in sub-Saharan Africa. This study evaluated the effect of a collaborative learning–based nutrition education intervention on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria. Methods A quasi-experimental pretest–posttest control group design was employed among 274 adolescents aged 13–19 years enrolled in private secondary schools. Participants were assigned to either a collaborative learning–based nutrition education intervention or usual didactic instruction over eight weeks. Diet quality was assessed using the Diet Quality Questionnaire (DQQ), generating Dietary Diversity Score (DDS), NCD-Protect, NCD-Risk, and Global Dietary Recommendations (GDR) scores. Nutrition knowledge was assessed using a validated questionnaire. Within- and between-group changes were analyzed using paired and independent tests, and analysis of covariance (ANCOVA) adjusted for baseline values and sociodemographic factors. Results Compared with controls, the intervention group showed significantly greater improvements in NCD-Protect score (Δ = 3.03; p < 0.001), NCD-Risk score (Δ=−1.11; p < 0.001), and GDR score (Δ = 4.14; p < 0.001). Improvements in DDS were observed in both groups, with no significant between-group difference. Nutrition knowledge increased significantly in the intervention group (Δ = 9.39; p < 0.001). ANCOVA confirmed a strong independent intervention effect on GDR (ηp²=0.26) and nutrition knowledge (ηp²=0.13). Conclusion Collaborative learning–based nutrition education significantly improves diet quality and nutrition knowledge among adolescents and represents a promising school-based strategy for NCD prevention in settings undergoing dietary transition. adolescents diet quality nutrition education nutrition knowledge non-communicable diseases school-based intervention Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction 1.1 Study background Adolescence, defined by the World Health Organization as the period between 10 and 19 years of age, is a critical life stage marked by rapid physical growth, cognitive development, and the consolidation of health-related behaviours that track into adulthood [ 1 , 2 ]. During this period, individuals attain approximately 20% of adult height, 50% of adult body weight, and up to 60% of peak bone mass, resulting in substantially increased requirements for energy, protein, and micronutrients [ 3 ]. Inadequate nutrition during adolescence therefore has immediate consequences for growth and development and longer-term implications for cardiometabolic health. Globally, adolescents experience a high burden of poor diet quality. Since 1980, the prevalence of overweight and obesity among children and adolescents has more than tripled, with over 340 million individuals aged 5–19 years classified as overweight or obese by 2016 [ 4 ]. Low- and middle-income countries (LMICs), including those in sub-Saharan Africa, are increasingly affected by the double burden of malnutrition, characterized by the coexistence of undernutrition and rising rates of overweight, obesity, and diet-related noncommunicable diseases.[ 5 ]. In Nigeria, adolescent overweight prevalence ranges from 7.4% to 13.2%, while obesity prevalence ranges from 2.6% to 4.4%, alongside persistent micronutrient inadequacies [ 6 ]. These trends elevate lifetime risk for type 2 diabetes, cardiovascular disease, and certain cancers [ 1 ]. Despite their heightened nutritional vulnerability, adolescents have historically received less attention in nutrition policy and programming compared with younger children [ 1 ]. In Nigeria, common dietary practices among adolescents include breakfast skipping, frequent snacking, and high consumption of sugar-sweetened beverages and energy-dense, ultra-processed foods, all of which contribute to poor diet quality [ 7 , 8 ]. These behaviours are shaped by multiple social and environmental factors, including household income, parental education, food availability, and school food environments [ 9 ]. Importantly, inadequate nutrition knowledge has been identified as a key modifiable determinant of unhealthy dietary choices during adolescence, influencing food selection, portion size, and responsiveness to food marketing [ 10 , 11 ]. To quantify overall dietary patterns beyond single nutrients or foods, the concept of diet quality has gained prominence in nutrition research over the past three decades [ 12 , 13 ]. Composite diet quality indices provide an integrated assessment of dietary adequacy and alignment with dietary guidelines. The Diet Quality Questionnaire (DQQ) is a standardized, low-burden instrument developed for population-level assessment of diet quality and adherence to global dietary recommendations [ 14 ]. The DQQ generates indicators relevant to both micronutrient adequacy, such as dietary diversity and Minimum Dietary Diversity for Women (MDD-W), and noncommunicable disease prevention, including the NCD-Protect, NCD-Risk, and Global Dietary Recommendations (GDR) scores [ 14 , 15 ]. These indicators enable robust evaluation of diet quality changes in response to interventions, particularly in LMIC settings. School-based nutrition education has been widely promoted as a promising strategy to improve adolescent diet quality and nutrition knowledge, given the structured learning environment, consistent access to adolescents, and opportunities for peer influence [ 16 , 17 ]. However, many school-based programmes continue to rely on teacher-centred, didactic approaches that prioritize information delivery over active engagement. Such approaches have demonstrated limited effectiveness in producing sustained improvements in dietary behaviours and knowledge application [ 18 ]. Adolescents’ increasing autonomy, sensitivity to peer norms, and preference for interactive learning may reduce the impact of passive instructional methods [ 19 ]. In contrast, collaborative and active learning approaches characterized by peer interaction, group problem-solving, and shared responsibility for learning have been shown to enhance cognitive engagement, knowledge retention, and behaviour change across educational and health contexts [ 10 , 20 ]. Within nutrition education, such approaches may strengthen nutrition knowledge while simultaneously improving the translation of knowledge into healthier dietary choices. Nevertheless, evidence on the effectiveness of collaborative learning–based nutrition education interventions on objectively measured diet quality, particularly among adolescents in sub-Saharan Africa, remains limited. 1.2 Objectives of the Study The aim of the study is to evaluated the effect of a collaborative learning–based nutrition education intervention, compared with usual didactic instruction, on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria. By combining standardized diet quality indicators with assessments of nutrition knowledge, this study provides mechanistic insight into how pedagogical approaches influence dietary patterns and informs the design of scalable, school-based nutrition interventions for adolescents in LMIC settings. 2. Methodology 2.1 Study Design This study employed a quasi-experimental, pretest–posttest control group design to evaluate the effect of a collaborative learning–based nutrition education intervention on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria. The intervention was grounded in collaborative learning principles, emphasizing active student engagement, peer interaction, and shared problem-solving to facilitate knowledge acquisition and retention. Participants in the intervention group received structured, interactive nutrition education sessions delivered using collaborative learning techniques, including small-group discussions, peer-led activities, and participatory problem-solving exercises. In contrast, participants in the control group continued with the usual didactic teaching approach, characterized by teacher-centered instruction and passive learning, without exposure to collaborative or interactive nutrition education methods. Outcome measures, including diet quality indicators and nutrition knowledge scores, were assessed at baseline and at the end of the intervention period in both groups. This design enabled the evaluation of within-group changes over time as well as between-group differences, thereby isolating the effect of the collaborative learning–based nutrition education intervention. 2.2 Study Area and Population The study was conducted in Odeda Local Government Area (LGA), Ogun State, Nigeria. Odeda LGA is a semi-urban area with a population that reflects a mix of rural and peri-urban characteristics. The area was selected due to its nutritional challenges, including evidence of both undernutrition and rising overweight/obesity among adolescents. The study population consisted of adolescents aged 13–19 years enrolled in private secondary schools within the LGA. Adolescents in this age range were selected because of their unique physiological and psychosocial developmental needs, as well as their increased susceptibility to poor dietary behaviors. 2.3 Sample Size and Sampling Procedure 2.3.1 Sampling Techniques This study employed a pre-test/post-test quasi-experimental design with intervention and control groups to evaluate the effect of collaborative nutrition education grounded in collaborative learning principles, emphasizing active student engagement, peer interaction, and shared problem-solving to facilitate knowledge acquisition. The study population comprised adolescents aged 13–19 years enrolled in private secondary schools within the Opeji Zone of Odeda Local Government Area. A multistage sampling approach was applied. First, the Opeji Zone was purposively selected from the three administrative zones due to its diverse socio-demographic composition. Within this zone, Obantoko community was purposively chosen for its large population and representation of urban and peri-urban private schools. From the list of registered private secondary schools in Obantoko, two schools were randomly selected. Within each school, students were stratified by class level (Senior Secondary 1 to 3), and proportional allocation determined the number of participants per class. Students were then selected through simple random sampling, yielding a final total sample of 315 adolescents. Selected schools were assigned to either the intervention or control group, with the intervention group receiving collaborative nutrition education or the control group following the standard didactic curriculum. 2.4 Sample Size Determination The sample size was determined using power analysis for two independent groups, assuming a small-to-moderate effect size (Cohen’s d = 0.30), a 5% level of significance (α = 0.05), and 80% statistical power (β = 0.20), resulting in a minimum of 87 participants per group (174 total) [ 21 , 22 ]. To account for potential attrition, the sample was increased by 10%, yielding 193 participants. Furthermore, to adjust for clustering effects inherent in the school-based sampling design, a design effect DEFF = 1 + (m − 1) ρ was applied, increasing the final target sample size to 310 participants. Of these, 274 participants completed both baseline and endline assessments, corresponding to a completion rate of 88% (as shown in Fig. 1 .). 2.5 Study Criteria Inclusion criteria were adolescents aged 13–19 years enrolled in the selected schools who provided assent and whose parents/guardians gave informed consent. Exclusion criteria included adolescents with chronic health conditions or disabilities affecting dietary intake, those absent during baseline data collection, and those unwilling to participate in the intervention sessions. 2.6 Recruitment A list of registered private secondary schools was obtained from the Local Education Authority of Odeda Local Government Area, Ogun State. From this list, two private secondary schools were selected to participate in the study based on the adequacy of their adolescent student population to meet the required sample size. Allocation of the selected schools to the intervention or control arm was conducted using a simple random method (coin toss) to minimize allocation bias. Following school selection, meetings were held with school administrators to explain the study objectives and procedures and to obtain institutional permission. Class registers provided by the school administration were used to identify eligible students in SSS 1 to 3. All adolescents aged 13–19 years who met the inclusion criteria were approached and invited to participate in the study. Written informed consent was obtained from parents or guardians, and assent was obtained from the adolescents prior to enrolment. Although each school had an average enrolment of approximately 450 students, the study sample size was predetermined, and only the required number of participants was enrolled. A total of 310 adolescents were recruited at baseline, with 155 participants assigned to the intervention group and 155 to the control group. Some students were not enrolled or were excluded due to absence during data collection, refusal to provide consent or assent, illness, or loss to follow-up. Recruitment and enrolment procedures are summarized in the study flow diagram (Fig. 1 .). 2.7 Data Collection tools and techniques Diet quality was assessed using the Global Diet Quality Questionnaire (DQQ), a standardized, low-burden dietary assessment tool developed by the Global Diet Quality Project in collaboration with Gallup, the Harvard T.H. Chan School of Public Health, and the Global Alliance for Improved Nutrition (GAIN) [ 14 , 23 ]. The DQQ captures dietary patterns based on reported consumption of sentinel foods representing 29 food groups during the previous day and night and is designed for population-level assessment rather than individual nutrient intake estimation. The questionnaire was administered strictly according to the standardized protocol, without modification, to ensure methodological consistency and comparability. Diet quality indicators were derived a priori in accordance with the DQQ Indicator Guide [ 23 ]. Dietary diversity was assessed using the Dietary Diversity Score (DDS), calculated as the sum of ten predefined food groups consumed, yielding a score ranging from 0 to 10, with higher scores indicating greater dietary diversity. For female participants, the Minimum Dietary Diversity for Women (MDD-W) was additionally derived from the DDS and expressed as a binary indicator, with a value of 1 assigned if five or more of the ten food groups were consumed and 0 otherwise, reflecting a higher likelihood of adequate micronutrient intake at the population level [ 15 , 24 ]. Adherence to dietary components protective against non-communicable diseases was assessed using the NCD-Protect score, which awards one point for consumption of each of nine health-promoting food groups, producing a score from 0 to 9. Dietary exposure to foods recommended to be limited was assessed using the NCD-Risk score, based on consumption of eight food groups associated with increased NCD risk, with processed meat double-weighted, resulting in a score from 0 to 9, where higher values indicate greater dietary risk. An overall measure of alignment with global dietary recommendations was calculated using the Global Dietary Recommendations Score (GDRS), computed as (NCD-Protect − NCD-Risk) + 9 and transformed to a scale of 0 to 18, with higher scores indicating healthier dietary patterns. 14,23 All indicators were analyzed and interpreted at the population level. Nutrition knowledge was assessed using a modified version of a validated questionnaire originally developed by [ 25 ] and subsequently adapted by [ 2 ] for use in the Nigerian context. The questionnaire evaluated participants’ knowledge of healthy diet composition, basic nutrition principles, and the health consequences of unhealthy eating. The instrument comprised twenty (20) questions (Q1–Q20) covering food groups, nutrient functions, and recommended healthy eating practices. Each correct response was assigned a score of 1, while incorrect, “don’t know,” or missing responses were scored 0. Individual item scores were summed to generate a total nutrition knowledge score, with higher scores indicating greater nutrition knowledge. 2.8 Intervention procedure This study employed a quasi-experimental, pretest–posttest control group design to evaluate the effect of a collaborative learning–based nutrition education intervention on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria. Participants in the intervention group received a structured nutrition education package delivered using collaborative learning strategies, while the control group continued with the standard school curriculum delivered through conventional didactic teaching methods without exposure to collaborative learning. The intervention was implemented over eight consecutive weeks, with one 60-minute session per week conducted separately for each grade level to ensure age-appropriate delivery. Nutrition education sessions were held within regular classroom settings during school hours. The educational content was identical for both groups and was adapted from nationally and internationally recognized nutrition guidance documents, including the Food-Based Dietary Guidelines for Nigeria and national dietary and physical activity guidelines. However, the mode of delivery differed substantially between groups. In the intervention group, nutrition education was delivered using collaborative learning techniques designed to promote active participation, peer interaction, and shared problem-solving. These included buzz group discussions, reciprocal teaching using a Jigsaw design, problem-based learning through the “send-a-problem” approach, and role-playing activities that simulated real-life food choice and dietary decision-making scenarios. Students worked in small groups, assumed rotating roles, and collectively constructed knowledge through guided facilitation rather than passive reception of information. To reinforce learning, participants produced session summaries in the form of wall newspapers at the end of each session. The control group received the same nutrition education content, delivered over the same duration and frequency, but through traditional teacher-led lectures consistent with routine classroom instruction. These sessions relied primarily on verbal explanations and textbook-based teaching, without structured peer interaction, group tasks, or collaborative activities. All sessions were facilitated by trained nutritionists and dietitians using standardized educational materials, including posters, leaflets, pamphlets, PowerPoint presentations, and short educational videos. The nutrition education package was developed by the research team and reviewed by academic and professional experts in nutrition prior to implementation. Intervention fidelity, participant engagement, and adherence to the delivery protocol were monitored throughout the intervention period using structured observational checklists. To minimize contamination, intervention and control participants were drawn from different schools. Follow-up data were collected four weeks after completion of the eight-week intervention using the same instruments administered at baseline to assess changes in diet quality and nutrition knowledge attributable to the intervention. 2.9 Nutrition Education Guide A structured, curriculum-based Nutrition Education Guide developed specifically for this study guided the nutrition education intervention. The guide was designed to ensure content validity, logical sequencing, and consistency across sessions. It was adapted from authoritative national and international nutrition education frameworks, including the Food-Based Dietary Guidelines for Nigeria, MyPlate, Kenya’s National Dietary and Physical Activity Guidelines, and established clinical nutrition texts [ 26 , 30 ]. These sources were selected to ensure scientific rigor, cultural relevance, and alignment with global recommendations for adolescent nutrition. The guide was structured into eight progressive lessons, each building on the previous session to promote cumulative learning and reinforcement of key concepts. Initial sessions introduced the importance of healthy eating during adolescence, emphasizing links between diet, growth and development, academic performance, mental well-being, and long-term risk of diet-related non-communicable diseases. Subsequent lessons focused on understanding food groups, nutrient functions, and the principles of constructing a balanced diet using visual dietary models, enabling students to translate abstract nutrition concepts into practical meal choices. Mid-intervention sessions addressed applied dietary skills, including portion control, meal planning, interpretation of nutrition labels, and selection of healthier snack options. These components were intended to strengthen food literacy and empower adolescents to make informed choices within real-world food environments. Additional sessions emphasized hydration and healthy beverage choices, highlighting the role of water and the health risks associated with sugar-sweetened beverages. The guide also explicitly addressed frequent consumption of fast foods, discussing their nutritional composition, associated health risks, and practical strategies for making healthier alternatives. The final sessions focused on overcoming barriers to healthy eating, including social influences, economic constraints, and limited access to healthy foods. Students were encouraged to identify context-specific challenges and collaboratively develop feasible solutions, reinforcing problem-solving skills and personal agency. Throughout the curriculum, learning was supported using interactive discussions, visual aids, practical demonstrations, and scenario-based activities, which were particularly suited to the collaborative learning approach adopted in the intervention. Overall, the Nutrition Education Guide provided a comprehensive yet age-appropriate coverage of adolescent nutrition, integrating knowledge acquisition with practical skill development. Its structured progression, alignment with evidence-based dietary guidelines, and incorporation of participatory learning strategies made it adequate and appropriate for achieving the intervention objectives, particularly in improving diet quality and nutrition knowledge among in-school adolescents. 2.10 Data Analysis Statistical analyses were performed using SPSS version 27 (IBM Corp., Armonk, NY, USA). Categorical socio-demographic and socio-economic variables were summarized as frequencies and percentages, while continuous variables were presented as means and standard deviations. Baseline comparability between intervention and control groups was assessed using the Chi-square test, with Fisher’s exact test applied where expected cell counts were < 5. Normality was assessed using the Shapiro–Wilk test. Diet quality indicators and nutrition knowledge scores were analyzed using paired t-tests or Wilcoxon signed-rank tests for within-group comparisons, and independent samples t-tests or Mann–Whitney U tests for between-group comparisons of change scores, as appropriate. Results were presented in tables and figures. To control for baseline differences, analysis of covariance (ANCOVA) was conducted to examine post-intervention group differences while adjusting for baseline values and relevant covariates, including parental income, occupation, educational status, family size, birth order, and baseline nutrition knowledge. ANCOVA assumptions were verified and met. Partial eta squared (ηp²) was reported as an effect size measure. Diet quality indicators were computed using the Diet Quality Questionnaire (DQQ) framework. All tests were two-tailed, and statistical significance was set at p < 0.05. 2.11 Ethical Considerations and Approval Ethical clearance for the study was obtained from the Health Research Ethics Committee of the Federal Medical Centre, Abeokuta (FMCA/470/HREC/17/2024/30). Prior to data collection, detailed information about the study objectives, procedures, potential benefits, and participants’ rights was communicated to both students and their parents/guardians. Written informed consent was secured from parents or guardians, while informed assent was obtained from all adolescent participants. Participation was voluntary, with the right to withdraw at any stage without any consequences. Confidentiality was strictly maintained with anonymized codes instead of personal identifiers, and access to data was restricted to the research team only. The study involved minimal to no risk and was conducted in accordance with internationally accepted ethical standards for research involving human participants. 3. Results 3.1 Sociodemographic and economic characteristics of participants Table 1 shows the sociodemographic and economic characteristics of the participants overall and by study group. The mean age of participants was 14.82 ± 0.88 years, with a significant difference in age distribution between the intervention and control groups (P = 0.001), driven by a higher proportion of younger adolescents (ages 13–14) in the control group and a greater concentration of ages 15–17 in the intervention group. Gender distribution was comparable between groups, with no significant difference observed (P = 0.117). Ethnic composition was predominantly Yoruba across both groups, with no significant between-group difference (P = 0.542). Household position differed significantly between groups (P = 0.001), with first-born participants more common in the intervention group and last-born participants more frequent in the control group. Class level distribution did not differ significantly between groups (P = 0.074). Parental educational attainment showed significant group differences. A higher proportion of fathers and mothers in the intervention group had tertiary education compared with the control group (father: P = 0.001; mother: P = 0.003). Similarly, fathers’ occupation differed significantly between groups (P = 0.003), with administrative occupations more prevalent in the intervention group, whereas farming, craft, and related trades were more common in the control group. Mothers’ occupation also varied significantly (P = 0.001), with a higher proportion of professional and administrative occupations in the intervention group and a greater proportion of farming, craft-related work, and unemployment in the control group. Household monthly income differed significantly between groups (P = 0.001), with a larger proportion of households earning more than ₦100,000 in the intervention group compared with the control group. Living arrangement did not differ significantly between groups (P = 0.098), with the majority of participants residing with both parents. The number of household members differed significantly (P = 0.001), with larger household sizes more common in the control group, whereas the number of siblings was comparable between groups (P = 0.295). Table 1 Sociodemographic and economic characteristics of the Adolescent Group Variable Aggregate N (%) Intervention Group N (%) Control Group N (%) χ² P.Value Age 13 18 (6.6) 4 (3) 14 (11) 56.680 b 0.001* 14 68 (24.8) 18 (12) 50 (40) 15 142 (51.8) 86 (57) 56 (45) 16 38 (13.9) 34 (23) 4 (3) 17 6 (2.2) 6 (4) 0 (0) 18 2 (0.7) 2 (1) 0 (0) Total 274 (100) 150 (100) 124 (100) Mean ± S.D 14.82 ± 0.88 Gender Male 136 (49.6) 68 (45) 68 (55) 2.453 a 0.117 Female 138 (50.4) 82 (55) 56 (45) Total 274 (100) 150 (100) 124 (100) Ethnicity Yoruba 260 (100) 144 (96) 116 (94) 1.226 b 0.542 Igbo 8 (2.9) 4 (3) 4 (3) Hausa 6 (2.2) 2 (1) 4 (3) Total 274 (100) 150 (100) 124 (100) Household Position Only born 4 (1.5) 0 (0) 4 (3) 45.323 b 0.001* First born 80 (29.2) 64 (43) 16 (13) Middle born 110 (40.1) 62 (41) 48 (39) Last born 80 (29.2) 24 (16) 56 (45) Total 274 (100) 150 (100) 124 (100) Class Level SS1 90 (32.8) 47 (31) 43 (35) 5.217 a 0.074 SS2 113 (41.2) 56 (37) 57 (46) SS3 71 (25.9) 47 (31) 24 (19) Total 274 (100) 150 (100) 124 (100) Father Highest Level of Education No formal education 8 (2.9) 0 (0) 8 (6) 39.099 b 0.001* Primary education 4 (1.5) 4 (3) 0 (0) Secondary education 46 (16.8) 10 (7) 36 (29) Tertiary education 216 (78.8) 136 (91) 80 (65) Total 274 (100) 150 (100) 124 (100) Mother Highest Level of Education No formal education 4 (1.5) 0 (0) 4 (3) 14.099 b 0.003* Primary education 10 (3.6) 6 (4) 4 (3) Secondary education 28 (10.2) 8 (5) 20 (16) Tertiary education 232 (84.7) 136 (91) 96 (77) Total 274 (100) 150 (100) 124 (100) Father's Occupation Administrator 126 (46) 82 (550 44 (35) 13.679 a 0.003* Professional 62 (22.6) 34 (23) 28 (23) Farmer, Craft and Related Trades Workers 74 (27) 30 (20) 44 (35) Armed Forces Occupations 12 (4.4) 4 (3) 8 (6) Total 274 (100) 150 (100) 124 (100) Mother's Occupation Administrator 104 (38) 64 (43) 40 (32) 26.577 b 0.001* Professional 56 (20.4) 40 (27) 16 (13) Farmer, Craft and Related Trades Workers 102 (37.2) 46 (31) 56 (45) Armed Forces Occupations 4 (1.5) 0 (0) 4 (3) Unemployed 8 (2.9) 0 (0) 8 (6) Total 274 (100) 150 (100) 124 (100) Household Monthly Income Less than 20,000 6 (2.2) 2 (1) 4 (3) 16.311 b 0.001* 20,000–50,000 18 (6.6) 2 (1) 16 (13) 50,001–100,000 28 (10.2) 16 (11) 12 (10) More than 100,000 222 (81) 130 (87) 92 (74) Total 274 (100) 150 (100) 124 (100) Who you live with Both Parent 224 (81.8) 128 (85) 96 (77) 6.294 b 0.098 Mother only 30 (10.9) 14 (9) 16 (13) Guardian 10 (3.6) 2 (1) 8 (6) Other relatives 10 (3.6) 6 (4) 4 (3) Total 274 (100) 150 (100) 124 (100) Number of Household Member 0–4 54 (19.7) 30 (20) 24 (19) 15.260 a 0.001* 5–8 208 (75.9) 120 (80) 88 (71) 9–12 12 (4.4) 0 (0) 12 (10) Total 274 (100) 150 (100) 124 (100) Mean ± S.D 5.37 ± 1.53 Number of Siblings 0–4 250 (91.2) 138 (92) 112 (90) 2.441 b 0.295 5–8 22 (8) 10 (7) 12 (10) 9–12 2 (0.7) 2 (1) 0 (0) Total 274 (100) 150 (100) 124 (100) Mean ± S.D 2.63 ± 1.46 a χ² Chi-square, b Fisher’s Exact test, and Asterisk (*) signify statistical significant differences at p < 0.05 3.2 Diet Quality of the Adolescent Table 2 presents within-group changes in diet quality indicators from baseline to endline for the intervention and control groups. In the intervention group, dietary diversity score (DDS) increased significantly from a mean of 4.92 ± 1.87 at baseline to 5.60 ± 1.77 at endline (mean difference [MD]: 0.68; 95% CI: 0.36, 0.99; P < 0.001). A smaller but statistically significant increase in DDS was also observed in the control group, from 5.32 ± 1.85 to 5.74 ± 1.53 (MD: 0.42; 95% CI: 0.06, 0.78; P < 0.001). The NCD-Protect score increased markedly in the intervention group, rising from 2.40 ± 1.54 at baseline to 6.32 ± 1.60 at endline (MD: 3.83; 95% CI: 3.48, 4.19; P < 0.001). In contrast, the control group showed a smaller increase from 3.16 ± 2.18 to 3.96 ± 1.36, which was not statistically significant (MD: 0.79; 95% CI: 0.33, 1.27; P = 0.361). The NCD-Risk score decreased significantly in the intervention group, declining from 3.33 ± 2.19 at baseline to 2.27 ± 1.43 at endline (MD: −1.07; 95% CI: −1.49, − 0.64; P = 0.001). In the control group, the NCD-Risk score remained essentially unchanged between baseline and endline (3.77 ± 2.37 vs. 3.81 ± 2.07), with a minimal mean difference (MD: 0.40; 95% CI: −0.45, 0.53; P = 0.007). The Global Dietary Recommendation (GDR) score increased substantially in the intervention group from 8.07 ± 2.78 at baseline to 12.97 ± 1.86 at endline (MD: 4.90; 95% CI: 4.34, 5.46; P < 0.001). In comparison, the control group exhibited a modest increase from 8.40 ± 3.18 to 9.15 ± 2.62, which was not statistically significant (MD: 0.76; 95% CI: 0.03, 1.49; P = 0.885). The between-group analysis showed that the intervention group experienced significantly greater improvements than the control group in NCD–protect score (CMD = 3.03; 95% CI: 2.46–3.61; p < 0.001), NCD–risk score (CMD = − 1.11; 95% CI: −1.75 to − 0.46; p < 0.001), and global dietary recommendation (GDR) score (CMD = 4.14; 95% CI: 3.24–5.04; p < 0.001). However, the between-group change in dietary diversity score (DDS) was not statistically significant (CMD = 0.26; 95% CI: −0.21–0.74; p = 0.280). Figure 2 . Table 2 Diet Quality of the Adolescent Group Intervention Group Control Group Change Baseline Mean ± SD Endline Mean ± SD MD 95% CI of the difference (LL-UL) P.Value ↕ Baseline Mean ± SD Endline Mean ± SD MD 95% CI of the difference (LL-UL) P.Value ↕ CMD (95% CI of the DID) LL-UL P.Value ↓ N 150 124 DDS (0–10)a 4.92 ± 187 5.60 ± 1.77 0.68 0.36–0.99 < 0.001 5.32 ± 1.85 5.74 ± 1.53 0.42 0.06–0.78 < 0.001 0.26 (-0.213–0.735) 0.280 NCD-Protect Score (0–9)b 2.40 ± 1.54 6.32 ± 1.60 3.83 3.48–4.19 < 0.001 3.16 ± 2.18 3.96 ± 1.36 0.79 0.33–1.27 0.361 3.03 (2.459–3.611) < 0.001 NCD-Risk Score (0–9)c 3.33 ± 2.19 2.27 ± 1.43 -1.07 -1.49–0.64 0.001 3.77 ± 2.37 3.81 ± 2.07 0.40 -0.45–0.53 0.007 -1.11 (-1.748 - -4.655) < 0.001 GDR Score (0–18)d 8.07 ± 2.78 12.97 ± 1.86 4.90 4.34–5.46 < 0.001 8.40 ± 3.18 9.15 ± 2.62 0.76 0.03–1.49 0.885 4.14 (3.242–5.042) < 0.001 Statistical Test: ↕ Paired T Test, ↓ Independent Test SD, Standard Deviation; MD, Mean Difference; CI, Confidence Interval; LL, Lower Limit; UL, Upper Limit; N, Frequency; DID, Difference in difference; CMD, Change mean difference; GDR, global dietary recommendations; NCD, non-communicable disease. a Dietary diversity Score (DDS) includes ten food groups: (1) grains, white roots and tuber, and plantains; (2) pulses (beans, peas and lentils); (3) nuts and seeds; (4) dairy; (5) meat, poultry and fish; (6) eggs; (7) dark green leafy vegetables; (8) other vitamin A-rich fruits and vegetables; (9) other vegetables; (10) other fruits. b NCD – protect score measures adherence to global dietary recommendations on foods to consume: (1) whole grains; (2) pulses; (3) nuts and seeds; (4) vitamin A-rich orange vegetables; (5) dark green leafy vegetables; (6) other vegetables; (7) vitamin A-rich fruits; (8) citrus; (9) other fruits. c NCD – risk score measures adherence to global dietary recommendations on foods to limit including: (1) soft drinks; (2) baked/grain-based sweets; (3) other sweets; (4) processed meats; (5) unprocessed meat; (6) deep fried food; (7) fast food and instant noodles; (8) packaged ultra-processed salty snacks. d GDR score = (NCD – Protect – NCD – Risk) + 9; measures adherence to global dietary recommendations protective against non-communicable diseases. 3.3 Diet Quality Indicators NCD Protect (+), NCD Risk (-), and Minimum Dietary Diversity for women of participant Figure 3 a. present at baseline, a high proportion of participants in the private school intervention group consumed at least one starchy staple (98%) and at least one animal-source food (94%), while consumption of at least one vegetable was reported by 84% and at least one fruit by 31%. At endline, the proportion consuming at least one vegetable increased to 94%, fruit consumption increased to 55%, and intake of pulses, nuts, or seeds increased from 50% to 58%. Pulse consumption specifically increased from 25% at baseline to 47% at endline, and whole-grain consumption increased modestly from 14% to 19%. Consumption of at least one vegetable or fruit declined from 88% at baseline to 58% at endline. Figure 3 b. shows NCD risk indicators, processed meat consumption increased from 23% at baseline to 40% at endline. In contrast, the proportion consuming salty or fried snacks declined from 61% to 29%, deep-fried food from 47% to 40%, sweet foods from 75% to 35%, and soft drinks from 52% to 35%. The proportion reporting zero vegetable or fruit consumption increased from 13% at baseline to 90% at endline. 3.4 Nutrition Knowledge Table 3 presents significant differences in general nutrition knowledge scores among participants, particularly between intervention and control groups. A similar trend was observed in private schools, where the intervention group’s mean score significantly increased from 63.64 (SD = 10.31) to 73.03 (SD = 12.31), with a mean difference of 9.39 (95% CI: 5.486, 13.302; p > 0.05), confirming the intervention's effectiveness. The control group, however, showed minimal and non-significant improvement, with scores rising from 51.87 (SD = 17.55) to 53.48 (SD = 17.17), resulting in a mean difference of 1.61 (95% CI: -4.559, 7.785; p = 0.606). This highlights the limited impact of nutrition knowledge without targeted intervention. The between-group change mean difference (difference-in-differences) indicated a significantly greater improvement in nutrition knowledge in the intervention group compared with the control group (CMD = 7.94; 95% CI: 2.18–13.69; p = 0.007) Fig. 4 . Table 3 Nutrition Knowledge of the participant Group Time N Mean SD M.D 95% CI (LL - UL) P.Value ↕ CMD (95% CI of the DID) LL-UL P.Value ↓ Intervention Baseline 150 63.64 10.31 9.39 5.486–13.302 < 0.001* 7.94 (2.183–13.687) 0.007* Endline 150 73.03 12.31 Control Baseline 124 51.87 17.55 1.61 -4.559–7.785 0.606 7.78 (3.401–12.161) < 0.001* Endline 124 53.48 17.17 Statistical Test: ↕ Paired T Test, ↓ Independent Test N, Frequency; M, Mean, SD, Standard Deviation; M.D, Mean Difference; CI, Confidence Interval; LL, Lower Limit; UL, Upper Limit; N, Frequency; DID, Difference in difference; CMD, Change mean difference a Paired T Test *Signifies Statistical Significant difference at p < 0.05 3.5 ANCOVA of Global Dietary Recommendation (GDR) Score, Diet Diversity (DD) Score, and Nutrition Knowledge (NK) Score by Sociodemographic and Economic Characteristics Table 4 a shows the results after adjustment for baseline GDR scores. ANCOVA demonstrated a significant main effect of intervention group on post-intervention GDR scores ( F (1, 225) = 77.72, p < 0.001), corresponding to a large effect size (partial η² = 0.26). None of the sociodemographic or economic characteristics including household size, gender, parental occupation, or household income were independently associated with post-intervention GDR scores ( p > 0.05). The overall model was statistically significant ( F (48, 225) = 5.69, p < 0.001) and explained approximately 45% of the variance in GDR outcomes (adjusted R² = 0.45), indicating a robust intervention effect independent of baseline dietary patterns. As shown in Table 4 b, baseline DD score significantly predicted post-intervention DD score ( F (1, 225) = 50.23, p < 0.001), corresponding to a large effect size (partial η² = 0.18). Significant main effects were observed for household size ( p < 0.001), household income ( p = 0.001), gender ( p = 0.002), father’s occupation ( p < 0.001), and mother’s occupation ( p = 0.038). Although the main effect of intervention group was not statistically significant ( p = 0.628), several moderate-to-large interaction effects were identified, including group × gender (partial η² = 0.09) and group × father’s occupation (partial η² = 0.26). These findings indicate heterogeneous intervention effects, with improvements in dietary diversity concentrated within specific sociodemographic subgroups. Overall, the DD score model explained 54.3% of the variance in post-intervention dietary diversity (adjusted R² = 0.54). As shown in Table 4 c, after adjustment for baseline nutrition knowledge, a significant intervention effect on post-intervention knowledge scores was observed ( F (1, 225) = 34.56, p < 0.001), corresponding to a large effect size (partial η² = 0.13). Baseline nutrition knowledge remained a significant covariate ( F (1, 225) = 9.96, p = 0.002). Father’s occupation was independently associated with nutrition knowledge outcomes ( F (3, 225) = 3.43, p = 0.018). In addition, significant interaction effects were observed between intervention group and both father’s occupation ( F (2, 225) = 5.47, p = 0.005) and mother’s occupation ( F (2, 225) = 7.53, p = 0.001), suggesting that intervention effectiveness varied across parental occupational strata. The final model accounted for 63.5% of the variance in post-intervention nutrition knowledge scores (adjusted R² = 0.64). Table 4 a ANCOVA of Global Dietary Recommendation (GDR) Score by Sociodemographic and Economic Characteristics Source df F p-value ηp² Baseline GDR score 1,225 0.01 0.944 < 0.001 Household size 1,225 1.16 0.284 0.005 Monthly household income 3,225 2.30 0.079 0.030 Gender 1,225 1.01 0.317 0.004 Father’s occupation 3,225 1.43 0.235 0.019 Mother’s occupation 4,225 1.66 0.161 0.029 Group (intervention vs control) 1,225 77.72 < 0.001* 0.257 Model fit: F (48, 225) = 5.69, p < 0.001; Adjusted R² = 0.452 *Signifies Statistical Significant difference at p < 0.05 Table 4 b ANCOVA of Diet Diversity (DD) Score by Sociodemographic and Economic Characteristics Source df F p-value ηp² Baseline DD score 1,225 50.23 < 0.001* 0.182 Household size 1,225 12.57 < 0.001* 0.053 Monthly household income 3,225 5.75 0.001* 0.071 Gender 1,225 9.79 0.002* 0.042 Father’s occupation 3,225 14.13 < 0.001* 0.159 Mother’s occupation 4,225 2.59 0.038* 0.044 Group (intervention vs control) 1,225 0.24 0.628 0.001 Group × Gender 1,225 21.93 < 0.001* 0.089 Group × Father’s occupation 2,225 38.82 < 0.001* 0.257 Income × Father’s occupation 1,225 19.17 < 0.001* 0.078 Gender × Mother’s occupation 2,225 5.62 0.004* 0.038 Father’s × Mother’s occupation 4,225 3.45 0.009* 0.058 Gender × Father’s × Mother’s occupation 2,225 12.21 < 0.001* 0.098 Model fit: F (48, 225) = 7.75, p < 0.001; Adjusted R² = 0.543 *Signifies Statistical Significant difference at p < 0.05 Table 4 c ANCOVA of Nutrition Knowledge (NK) Score by Sociodemographic and Economic Characteristics Source df F p-value ηp² Baseline NK score 1,225 9.96 0.002* 0.042 Household size 1,225 0.64 0.423 0.003 Monthly household income 3,225 0.39 0.760 0.005 Gender 1,225 0.10 0.753 < 0.001 Father’s occupation 3,225 3.43 0.018* 0.044 Mother’s occupation 4,225 0.43 0.784 0.008 Group (intervention vs control) 1,225 34.56 < 0.001* 0.133 Group × Father’s occupation 2,225 5.47 0.005* 0.046 Group × Mother’s occupation 2,225 7.53 0.001* 0.063 Father’s × Mother’s occupation 4,225 7.96 < 0.001* 0.124 Group × Gender × Father’s occupation 2,225 3.13 0.046* 0.027 Model fit: F (48, 225) = 10.88, p < 0.001; Adjusted R² = 0.635 *Signifies Statistical Significant difference at p < 0.05 4. Discussion This study demonstrates that a school-based collaborative learning–based nutrition education intervention produced meaningful improvements in multiple dimensions of diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria. Relative to usual didactic instruction, the intervention resulted in significantly higher dietary diversity, greater consumption of NCD-protective foods, improved adherence to Global Dietary Recommendations (GDR), and reduced intake of NCD-risk foods. Together, these findings provide robust evidence that pedagogical approach not merely content matters for improving adolescent diet quality in low- and middle-income settings. Dietary diversity, a widely used proxy for micronutrient adequacy and overall diet quality, improved in both study arms, but gains were consistently greater among adolescents exposed to the collaborative learning intervention. This pattern aligns with evidence that nutrition education enhances adolescents’ awareness of food groups and supports more varied food choices when learning is interactive and contextually relevant [ 31 ]. Similar associations between improved nutrition knowledge and higher dietary diversity have been reported among Nigerian adolescents, reinforcing the role of structured education in promoting food variety [ 32 ]. Beyond diversity, the intervention produced a pronounced increase in the NCD-Protect score, reflecting higher intake of fruits, vegetables, legumes, and whole grains—foods strongly associated with reduced cardiometabolic risk. In contrast, changes in the control group were modest and non-significant. Systematic reviews consistently show that school-based nutrition education can increase adolescents’ consumption of fruits and vegetables, particularly when programs emphasize skills, peer engagement, and active learning [ 33 ]. Concurrently, the observed reduction in NCD-Risk scores among intervention participants indicates decreased consumption of unhealthy foods, consistent with prior evidence linking nutrition education to lower intake of sugar-sweetened beverages, fried foods, and energy-dense snacks among youth [ 34 ]. Improvements in the GDR score suggest that the intervention supported broader alignment with holistic dietary recommendations rather than isolated food substitutions. This finding is particularly relevant for adolescent nutrition, as composite indices better capture real-world dietary patterns than single-nutrient outcomes. Meta-analytic evidence indicates that comprehensive school-based nutrition programs can improve overall dietary adherence, even when effects on individual food groups vary [ 33 ]. At baseline, adolescents particularly in private schools exhibited high consumption of staples and animal-source foods but suboptimal intake of fruits and vegetables, a pattern widely observed among adolescents in LMICs [ 35 ]. By endline, marked increases were observed in vegetable and fruit consumption, as well as in pulses, nuts, seeds, and whole grains, all of which contributed to improved NCD-Protect scores. These shifts indicate that the intervention effectively promoted protective food choices central to long-term NCD prevention. Improved dietary diversity and protective food intake are consistent with evidence that education-driven behavior change strategies can enhance micronutrient adequacy by increasing food group variety [ 36 ]. Although the Minimum Dietary Diversity for Women (MDD-W) was originally developed for women aged 15–49 years, its conceptual basis that consumption of multiple food groups signals greater micronutrient adequacy is equally relevant for adolescents [ 24 ]. Thus, observed improvements in dietary diversity likely reflect broader gains in nutrient adequacy. Mixed trends were observed for specific NCD-risk foods. While intake of salty snacks, sweet foods, fried foods, and sugar-sweetened beverages declined substantially and this consistent with prior school-based intervention evidence [ 37 ]. processed meat consumption increased. This likely reflects broader food environment influences, including availability, marketing, and social norms, which may not be fully addressed by education alone. Global evidence increasingly implicates ultra-processed foods, including processed meats, in declining diet quality and rising adolescent obesity and NCD risk [ 38 ]. Some inconsistencies in reported zero fruit or vegetable consumption across time points may reflect measurement nuances or recall variability rather than true behavioral reversal. Such discrepancies highlight the complexity of dietary assessment in adolescents and reinforce the value of validated, food-group–based indicators such as those embedded in the DQQ framework [ 39 ]. The intervention produced a substantial and statistically significant improvement in nutrition knowledge, with a mean increase of over nine points in the intervention group, while changes in the control group were negligible. These findings align with growing evidence that school-based nutrition education, particularly when interactive and participatory, effectively enhances adolescents’ nutrition knowledge [ 40 ]. Collaborative learning likely amplified knowledge gains through peer discussion, shared problem-solving, and active engagement, mechanisms known to improve comprehension and retention beyond traditional lecture-based instruction [ 20 ]. Umbrella reviews of school-based healthy eating interventions consistently report improvements in nutrition knowledge and attitudes when education is embedded within participatory learning environments [ 41 ]. Although knowledge gains do not always translate directly into sustained behavior change, nutrition knowledge is a necessary precursor for informed food choice and dietary self-regulation [ 42 ]. Evidence from sub-Saharan Africa further supports the generalizability of these findings, with structured school-based nutrition education improving knowledge among children and adolescents across diverse contexts [ 43 ]. After adjustment for baseline values and covariates, the intervention exerted a strong independent effect on post-intervention GDR scores, with a large effect size, indicating that collaborative learning–based nutrition education robustly improved overall dietary adherence irrespective of sociodemographic background. Similar findings have been reported in systematic reviews demonstrating favorable changes in composite diet quality indices following school-based interventions [ 33 ]. The absence of significant sociodemographic effects on GDR outcomes suggests that school-based nutrition education may act as an equalizer, mitigating disparities in diet quality linked to household income or parental occupation [ 44 ]. In contrast, dietary diversity outcomes were more strongly influenced by baseline diet and socioeconomic factors, underscoring the role of food access and household resources in shaping diet variety [ 45 ]. For nutrition knowledge, the intervention remained highly effective after adjustment, with baseline knowledge and parental occupation emerging as important predictors. These findings echo prior research showing that parental socioeconomic status shapes adolescents’ exposure to, and application of, nutrition information [ 46 ]. This study demonstrates that collaborative learning–based nutrition education grounded in the Health Belief Model can meaningfully improve diet quality and nutrition knowledge among in-school adolescents. For public health nutrition practice, the findings support the integration of interactive, school-based nutrition education into routine adolescent health services as a preventive strategy against poor diet quality and future non-communicable diseases. The intervention’s effectiveness independent of baseline dietary patterns suggests its applicability across diverse adolescent populations. From a policy perspective, the results align with Nigeria’s National School Health Policy, reinforcing schools as critical platforms for health promotion. The differential effects observed across sociodemographic groups highlight the need for context-sensitive implementation, with adaptations that account for household socioeconomic conditions. Scaling such interventions through curriculum integration and collaboration between health and education sectors may strengthen adolescent nutrition outcomes in resource-limited settings. 4.1 Strengths, Limitations, and Potential Biases Key strengths of this study include the quasi-experimental pre–post design with a control group and the use of ANCOVA to adjust for baseline outcomes and sociodemographic factors, enhancing internal validity. The application of the Global Diet Quality Questionnaire, a standardized and internationally comparable tool, strengthened measurement rigor and alignment with global nutrition frameworks. The theory-driven, participatory intervention further enhanced relevance and engagement. However, limitations should be noted. The non-randomized design may allow residual confounding despite statistical adjustment. Dietary intake and nutrition knowledge were self-reported and may be subject to recall or social desirability bias. The relatively short follow-up limits inference on the sustainability of effects, and the restriction to selected schools in one local government area may limit generalizability to other settings. Information bias may have arisen from self-reported dietary intake and knowledge, particularly at endline when intervention participants may have been more aware of recommended behaviors. Performance bias is possible, as the intervention group received greater interaction and engagement through collaborative learning compared with controls. Selection bias related to school or group allocation cannot be fully excluded, although baseline adjustment reduced this risk. The observed interaction effects by parental occupation and household characteristics suggest differential responsiveness, indicating potential effect modification rather than uniform intervention impact. These biases should be considered when interpreting the magnitude of observed effects and in planning future implementations. 4.2 Implications for Future Research Future studies should assess the long-term sustainability of improvements in diet quality and nutrition knowledge through extended follow-up periods. Randomized or cluster-randomized designs across multiple regions would strengthen causal inference and generalizability. Mixed-methods research could elucidate contextual mechanisms underlying differential effects by socioeconomic status, while implementation research is needed to evaluate scalability, cost-effectiveness, and integration within national school health systems. 5. Conclusion This study provides evidence that a collaborative learning–based nutrition education intervention can significantly improve overall diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria. Adolescents exposed to the intervention demonstrated greater adherence to global dietary recommendations, increased consumption of NCD-protective foods, reduced intake of NCD-risk foods, and substantially higher nutrition knowledge compared with peers receiving usual didactic instruction. These findings underscore the importance of pedagogical approach in shaping adolescents’ dietary behaviours and nutrition literacy, particularly in settings undergoing rapid dietary transition. By combining standardized diet quality indicators with nutrition knowledge outcomes, this study offers mechanistic insight into how interactive, learner-centred education may facilitate healthier dietary patterns during adolescence a critical window for long-term NCD prevention. Declarations Acknowledgements None Author contributions declaration Dare D. Adémiluyi, Ojo-Adalumo A. Rhoda, Esther D.Olubiyi, Akinade E. Ogunniyi. All authors contributed equally to the manuscript Funding None Data availability Statement All data produced are available within the manuscript. Clinical Trial Number: Not applicable Ethics approval and consent to participate Ethical clearance for the study was obtained from the Health Research Ethics Committee of the Federal Medical Centre, Abeokuta (FMCA/470/HREC/17/2024/30). Prior to data collection, detailed information about the study objectives, procedures, potential benefits, and participants’ rights was communicated to both students and their parents/guardians. Written informed consent was secured from parents or guardians, while informed assent was obtained from all adolescent participants. Participation was voluntary, with the right to withdraw at any stage without any consequences. Confidentiality was strictly maintained with anonymized codes instead of personal identifiers, and access to data was restricted to the research team only. The study involved minimal to no risk and was conducted in accordance with internationally accepted ethical standards for research involving human participants. Consent for publish All authors reviewed the manuscript’s final version and approved it for submission for publication. Competing interests The authors declare no competing interests References Norris SA, Frongillo EA, Black MM, Dong Y, Fall C, Lampl M, et al. Nutrition in adolescent growth and development. Lancet. 2022;399(10320):172–84. 10.1016/S0140-6736(21)01590-7 . Ademiluyi D, Uthman-Akinhanmi Y. Diet quality and nutrition knowledge of in-school adolescents in private and public schools at Odeda local government area. Eur J Health Res. 2025;11(1):e3277. 10.32457/ejhr.v11i1.3277 . USAID. Adolescent nutrition 2000–2017: DHS data on adolescents age 15–19. 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Minimum dietary diversity for adolescents: multicountry analysis to define food group thresholds predicting micronutrient adequacy among girls and boys aged 10–19 years. Curr Dev Nutr. 2024;8(3):102097. 10.1016/j.cdnut.2024.102097 . Raut S, KC D, Singh DR, et al. Effect of nutrition education intervention on nutrition knowledge, attitude, and diet quality among school-going adolescents: a quasi-experimental study. BMC Nutr. 2024;10:35. 10.1186/s40795-024-00850-0 . Samad N, Bearne L, Noor FM, Akter F, Parmar D. School-based healthy eating interventions for adolescents aged 10–19 years: an umbrella review. Int J Behav Nutr Phys Act. 2024;21(1). 10.1186/s12966-024-01668-6 . Kyere P, Veerman JL, Lee P, Stewart DE. Effectiveness of school-based nutrition interventions in sub-Saharan Africa: a systematic review. Public Health Nutr. 2020;23(14):2626–36. 10.1017/S1368980020000506 . Mogre V, Sefogah PE, Adetunji AW, Olalekan OO, Gaa PK, Ayettey Anie HN, et al. A school-based food and nutrition education intervention increases nutrition-related knowledge and fruit consumption among primary school children in northern Ghana. BMC Public Health. 2024;24(1). 10.1186/s12889-024-19200-7 . Mohammadi S, Koo HC. Editorial: school-based nutrition and physical activity interventions among children and adolescents. Front Public Health. 2025;13:1585511. 10.3389/fpubh.2025.1585511 . Sinthia SB, Tanveer AIA, Sujit KB, Hosna K, Md Rezaul K. Influence of socio-economic determinants on dietary diversity and nutritional status among young adults in Noakhali, Bangladesh. Asian J Food Res Nutr. 2023;2(4):863–877. Available from: https://www.journalajfrn.com/index.php/AJFRN/article/view/103 Sanlier N, Kocaay F, Kocabas S, Ayyildiz P. The effect of sociodemographic and anthropometric variables on nutritional knowledge and nutrition literacy. Foods. 2024;13(2):346. 10.3390/foods13020346 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Discover Public Health → Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviewers agreed at journal 25 Jan, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviews received at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviews received at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor invited by journal 18 Jan, 2026 Editor assigned by journal 13 Jan, 2026 Submission checks completed at journal 13 Jan, 2026 First submitted to journal 09 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8560558","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578584607,"identity":"da4a6ca4-0ba6-4183-848a-07af490e92f3","order_by":0,"name":"Dare Damilola Adémiluyi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYFCCBCAqYGBgAzEYKoCYmbmBCC0GMC1nQFoYidDCYABlMLaBGAS08LcnH93wwOBwYh978rMPH+fVRvO3A7X8qNiGU4vEmWdpNxKAWtp4nhnPnLnteO6Mw4wNjD1nbuO25kaOGUSLRIIxM++2Y7kNQC3MjG24tcjfyP8G1ZL+mfnvnGO58wlpMbiRwwbVkmMMDKua3A2EtBieeQZyWLpxG8+bYsaeYwdyNwK1HMTnF7njyc9u/qiwlp3fnr6Z4UdNXe6884cPPvhRgcf7UODYAKEPg8kDBNUDgT2UriNG8SgYBaNgFIwwAAB8qGMzlQAN4wAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Human Nutrition and Dietetics, College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Dare","middleName":"Damilola","lastName":"Adémiluyi","suffix":""},{"id":578584608,"identity":"3f908a48-0158-4dd7-8f9a-1ec82ff4d695","order_by":1,"name":"Ojo-Adalumo Ayobami Rhoda","email":"","orcid":"","institution":"College of Nursing 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14:26:23","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196278,"visible":true,"origin":"","legend":"","description":"","filename":"5e8eb0ba5d3c437b8fa18759b6426f671structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8560558/v1/6ad4de0510d33101d9831375.xml"},{"id":100897927,"identity":"d5fc9510-7892-46cc-a3f1-57dece1921a4","added_by":"auto","created_at":"2026-01-22 14:26:03","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":216257,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8560558/v1/828b1da95563a538c9ee7624.html"},{"id":100898048,"identity":"5a45fd4a-7a90-45ec-bd0f-c1902b53e3b0","added_by":"auto","created_at":"2026-01-22 14:26:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":121743,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the study\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8560558/v1/d92baa268429dea431e15735.jpg"},{"id":100897988,"identity":"fee1371d-92e0-4bd2-b3cb-4c38c097673f","added_by":"auto","created_at":"2026-01-22 14:26:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92363,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of a nutrition education intervention on adolescents’ diet quality indicators, as depicted in a bar chart.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8560558/v1/916b73be6fc398f8cf8e2de2.jpg"},{"id":100897994,"identity":"cbc382e0-a8fd-4042-ae90-a6a83f743826","added_by":"auto","created_at":"2026-01-22 14:26:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003e NCD Protect: Consumption of these food groups correlates positively with meeting the global dietary recommendations for health-protective foods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb.\u003c/strong\u003e NCD Risk: Consumption of these food groups correlates negatively with meeting the global dietary recommendations on dietary components to limit.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8560558/v1/793b7cb7191ec35f0df29a7d.png"},{"id":100897933,"identity":"693de039-76c8-473f-acff-0eed93806d57","added_by":"auto","created_at":"2026-01-22 14:26:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71516,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of the nutrition education intervention on adolescents’ nutrition knowledge, as shown by post-intervention changes\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8560558/v1/12dcdbd51d95929e15cd112f.jpg"},{"id":108440234,"identity":"b3e5b4a0-8ea9-4251-838e-1cafec00bcd2","added_by":"auto","created_at":"2026-05-04 16:34:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1163251,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8560558/v1/a303cccb-44aa-4004-8c4f-dc9d9c2dc14a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of a collaborative learning–based nutrition education intervention on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria: a quasi-experimental study","fulltext":[{"header":"1 Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Study background\u003c/h2\u003e \u003cp\u003eAdolescence, defined by the World Health Organization as the period between 10 and 19 years of age, is a critical life stage marked by rapid physical growth, cognitive development, and the consolidation of health-related behaviours that track into adulthood [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. During this period, individuals attain approximately 20% of adult height, 50% of adult body weight, and up to 60% of peak bone mass, resulting in substantially increased requirements for energy, protein, and micronutrients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Inadequate nutrition during adolescence therefore has immediate consequences for growth and development and longer-term implications for cardiometabolic health.\u003c/p\u003e \u003cp\u003eGlobally, adolescents experience a high burden of poor diet quality. Since 1980, the prevalence of overweight and obesity among children and adolescents has more than tripled, with over 340\u0026nbsp;million individuals aged 5\u0026ndash;19 years classified as overweight or obese by 2016 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Low- and middle-income countries (LMICs), including those in sub-Saharan Africa, are increasingly affected by the double burden of malnutrition, characterized by the coexistence of undernutrition and rising rates of overweight, obesity, and diet-related noncommunicable diseases.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In Nigeria, adolescent overweight prevalence ranges from 7.4% to 13.2%, while obesity prevalence ranges from 2.6% to 4.4%, alongside persistent micronutrient inadequacies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These trends elevate lifetime risk for type 2 diabetes, cardiovascular disease, and certain cancers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite their heightened nutritional vulnerability, adolescents have historically received less attention in nutrition policy and programming compared with younger children [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Nigeria, common dietary practices among adolescents include breakfast skipping, frequent snacking, and high consumption of sugar-sweetened beverages and energy-dense, ultra-processed foods, all of which contribute to poor diet quality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These behaviours are shaped by multiple social and environmental factors, including household income, parental education, food availability, and school food environments [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Importantly, inadequate nutrition knowledge has been identified as a key modifiable determinant of unhealthy dietary choices during adolescence, influencing food selection, portion size, and responsiveness to food marketing [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To quantify overall dietary patterns beyond single nutrients or foods, the concept of diet quality has gained prominence in nutrition research over the past three decades [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Composite diet quality indices provide an integrated assessment of dietary adequacy and alignment with dietary guidelines. The Diet Quality Questionnaire (DQQ) is a standardized, low-burden instrument developed for population-level assessment of diet quality and adherence to global dietary recommendations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The DQQ generates indicators relevant to both micronutrient adequacy, such as dietary diversity and Minimum Dietary Diversity for Women (MDD-W), and noncommunicable disease prevention, including the NCD-Protect, NCD-Risk, and Global Dietary Recommendations (GDR) scores [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These indicators enable robust evaluation of diet quality changes in response to interventions, particularly in LMIC settings.\u003c/p\u003e \u003cp\u003eSchool-based nutrition education has been widely promoted as a promising strategy to improve adolescent diet quality and nutrition knowledge, given the structured learning environment, consistent access to adolescents, and opportunities for peer influence [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, many school-based programmes continue to rely on teacher-centred, didactic approaches that prioritize information delivery over active engagement. Such approaches have demonstrated limited effectiveness in producing sustained improvements in dietary behaviours and knowledge application [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Adolescents\u0026rsquo; increasing autonomy, sensitivity to peer norms, and preference for interactive learning may reduce the impact of passive instructional methods [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, collaborative and active learning approaches characterized by peer interaction, group problem-solving, and shared responsibility for learning have been shown to enhance cognitive engagement, knowledge retention, and behaviour change across educational and health contexts [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Within nutrition education, such approaches may strengthen nutrition knowledge while simultaneously improving the translation of knowledge into healthier dietary choices. Nevertheless, evidence on the effectiveness of collaborative learning\u0026ndash;based nutrition education interventions on objectively measured diet quality, particularly among adolescents in sub-Saharan Africa, remains limited.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Objectives of the Study\u003c/h2\u003e \u003cp\u003eThe aim of the study is to evaluated the effect of a collaborative learning\u0026ndash;based nutrition education intervention, compared with usual didactic instruction, on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria.\u003c/p\u003e \u003cp\u003e \u003col style=\"list-style-type:lower-roman;\"\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBy combining standardized diet quality indicators with assessments of nutrition knowledge, this study provides mechanistic insight into how pedagogical approaches influence dietary patterns and informs the design of scalable, school-based nutrition interventions for adolescents in LMIC settings.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThis study employed a quasi-experimental, pretest\u0026ndash;posttest control group design to evaluate the effect of a collaborative learning\u0026ndash;based nutrition education intervention on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria. The intervention was grounded in collaborative learning principles, emphasizing active student engagement, peer interaction, and shared problem-solving to facilitate knowledge acquisition and retention. Participants in the intervention group received structured, interactive nutrition education sessions delivered using collaborative learning techniques, including small-group discussions, peer-led activities, and participatory problem-solving exercises. In contrast, participants in the control group continued with the usual didactic teaching approach, characterized by teacher-centered instruction and passive learning, without exposure to collaborative or interactive nutrition education methods. Outcome measures, including diet quality indicators and nutrition knowledge scores, were assessed at baseline and at the end of the intervention period in both groups. This design enabled the evaluation of within-group changes over time as well as between-group differences, thereby isolating the effect of the collaborative learning\u0026ndash;based nutrition education intervention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Area and Population\u003c/h2\u003e \u003cp\u003eThe study was conducted in Odeda Local Government Area (LGA), Ogun State, Nigeria. Odeda LGA is a semi-urban area with a population that reflects a mix of rural and peri-urban characteristics. The area was selected due to its nutritional challenges, including evidence of both undernutrition and rising overweight/obesity among adolescents. The study population consisted of adolescents aged 13\u0026ndash;19 years enrolled in private secondary schools within the LGA. Adolescents in this age range were selected because of their unique physiological and psychosocial developmental needs, as well as their increased susceptibility to poor dietary behaviors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sample Size and Sampling Procedure\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Sampling Techniques\u003c/h2\u003e \u003cp\u003eThis study employed a pre-test/post-test quasi-experimental design with intervention and control groups to evaluate the effect of collaborative nutrition education grounded in collaborative learning principles, emphasizing active student engagement, peer interaction, and shared problem-solving to facilitate knowledge acquisition. The study population comprised adolescents aged 13\u0026ndash;19 years enrolled in private secondary schools within the Opeji Zone of Odeda Local Government Area.\u003c/p\u003e \u003cp\u003eA multistage sampling approach was applied. First, the Opeji Zone was purposively selected from the three administrative zones due to its diverse socio-demographic composition. Within this zone, Obantoko community was purposively chosen for its large population and representation of urban and peri-urban private schools. From the list of registered private secondary schools in Obantoko, two schools were randomly selected. Within each school, students were stratified by class level (Senior Secondary 1 to 3), and proportional allocation determined the number of participants per class. Students were then selected through simple random sampling, yielding a final total sample of 315 adolescents. Selected schools were assigned to either the intervention or control group, with the intervention group receiving collaborative nutrition education or the control group following the standard didactic curriculum.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sample Size Determination\u003c/h2\u003e \u003cp\u003eThe sample size was determined using power analysis for two independent groups, assuming a small-to-moderate effect size (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.30), a 5% level of significance (α\u0026thinsp;=\u0026thinsp;0.05), and 80% statistical power (β\u0026thinsp;=\u0026thinsp;0.20), resulting in a minimum of 87 participants per group (174 total) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To account for potential attrition, the sample was increased by 10%, yielding 193 participants. Furthermore, to adjust for clustering effects inherent in the school-based sampling design, a design effect DEFF\u0026thinsp;=\u0026thinsp;1 + (m\u0026thinsp;\u0026minus;\u0026thinsp;1) ρ was applied, increasing the final target sample size to 310 participants. Of these, 274 participants completed both baseline and endline assessments, corresponding to a completion rate of 88% (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Study Criteria\u003c/h2\u003e \u003cp\u003eInclusion criteria were adolescents aged 13\u0026ndash;19 years enrolled in the selected schools who provided assent and whose parents/guardians gave informed consent. Exclusion criteria included adolescents with chronic health conditions or disabilities affecting dietary intake, those absent during baseline data collection, and those unwilling to participate in the intervention sessions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Recruitment\u003c/h2\u003e \u003cp\u003eA list of registered private secondary schools was obtained from the Local Education Authority of Odeda Local Government Area, Ogun State. From this list, two private secondary schools were selected to participate in the study based on the adequacy of their adolescent student population to meet the required sample size. Allocation of the selected schools to the intervention or control arm was conducted using a simple random method (coin toss) to minimize allocation bias.\u003c/p\u003e \u003cp\u003eFollowing school selection, meetings were held with school administrators to explain the study objectives and procedures and to obtain institutional permission. Class registers provided by the school administration were used to identify eligible students in SSS 1 to 3. All adolescents aged 13\u0026ndash;19 years who met the inclusion criteria were approached and invited to participate in the study. Written informed consent was obtained from parents or guardians, and assent was obtained from the adolescents prior to enrolment.\u003c/p\u003e \u003cp\u003eAlthough each school had an average enrolment of approximately 450 students, the study sample size was predetermined, and only the required number of participants was enrolled. A total of 310 adolescents were recruited at baseline, with 155 participants assigned to the intervention group and 155 to the control group. Some students were not enrolled or were excluded due to absence during data collection, refusal to provide consent or assent, illness, or loss to follow-up. Recruitment and enrolment procedures are summarized in the study flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Data Collection tools and techniques\u003c/h2\u003e \u003cp\u003eDiet quality was assessed using the Global Diet Quality Questionnaire (DQQ), a standardized, low-burden dietary assessment tool developed by the Global Diet Quality Project in collaboration with Gallup, the Harvard T.H. Chan School of Public Health, and the Global Alliance for Improved Nutrition (GAIN) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The DQQ captures dietary patterns based on reported consumption of sentinel foods representing 29 food groups during the previous day and night and is designed for population-level assessment rather than individual nutrient intake estimation. The questionnaire was administered strictly according to the standardized protocol, without modification, to ensure methodological consistency and comparability.\u003c/p\u003e \u003cp\u003eDiet quality indicators were derived \u003cem\u003ea\u003c/em\u003e priori in accordance with the DQQ Indicator Guide [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Dietary diversity was assessed using the Dietary Diversity Score (DDS), calculated as the sum of ten predefined food groups consumed, yielding a score ranging from 0 to 10, with higher scores indicating greater dietary diversity. For female participants, the Minimum Dietary Diversity for Women (MDD-W) was additionally derived from the DDS and expressed as a binary indicator, with a value of 1 assigned if five or more of the ten food groups were consumed and 0 otherwise, reflecting a higher likelihood of adequate micronutrient intake at the population level [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdherence to dietary components protective against non-communicable diseases was assessed using the NCD-Protect score, which awards one point for consumption of each of nine health-promoting food groups, producing a score from 0 to 9. Dietary exposure to foods recommended to be limited was assessed using the NCD-Risk score, based on consumption of eight food groups associated with increased NCD risk, with processed meat double-weighted, resulting in a score from 0 to 9, where higher values indicate greater dietary risk. An overall measure of alignment with global dietary recommendations was calculated using the Global Dietary Recommendations Score (GDRS), computed as \u003cem\u003e(NCD-Protect\u0026thinsp;\u0026minus;\u0026thinsp;NCD-Risk)\u0026thinsp;+\u0026thinsp;9\u003c/em\u003e and transformed to a scale of 0 to 18, with higher scores indicating healthier dietary patterns.\u003csup\u003e14,23\u003c/sup\u003e All indicators were analyzed and interpreted at the population level.\u003c/p\u003e \u003cp\u003eNutrition knowledge was assessed using a modified version of a validated questionnaire originally developed by [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and subsequently adapted by [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] for use in the Nigerian context. The questionnaire evaluated participants\u0026rsquo; knowledge of healthy diet composition, basic nutrition principles, and the health consequences of unhealthy eating. The instrument comprised twenty (20) questions (Q1\u0026ndash;Q20) covering food groups, nutrient functions, and recommended healthy eating practices. Each correct response was assigned a score of 1, while incorrect, \u0026ldquo;don\u0026rsquo;t know,\u0026rdquo; or missing responses were scored 0. Individual item scores were summed to generate a total nutrition knowledge score, with higher scores indicating greater nutrition knowledge.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Intervention procedure\u003c/h2\u003e \u003cp\u003eThis study employed a quasi-experimental, pretest\u0026ndash;posttest control group design to evaluate the effect of a collaborative learning\u0026ndash;based nutrition education intervention on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria. Participants in the intervention group received a structured nutrition education package delivered using collaborative learning strategies, while the control group continued with the standard school curriculum delivered through conventional didactic teaching methods without exposure to collaborative learning.\u003c/p\u003e \u003cp\u003eThe intervention was implemented over eight consecutive weeks, with one 60-minute session per week conducted separately for each grade level to ensure age-appropriate delivery. Nutrition education sessions were held within regular classroom settings during school hours. The educational content was identical for both groups and was adapted from nationally and internationally recognized nutrition guidance documents, including the Food-Based Dietary Guidelines for Nigeria and national dietary and physical activity guidelines. However, the mode of delivery differed substantially between groups. In the intervention group, nutrition education was delivered using collaborative learning techniques designed to promote active participation, peer interaction, and shared problem-solving. These included buzz group discussions, reciprocal teaching using a Jigsaw design, problem-based learning through the \u0026ldquo;send-a-problem\u0026rdquo; approach, and role-playing activities that simulated real-life food choice and dietary decision-making scenarios. Students worked in small groups, assumed rotating roles, and collectively constructed knowledge through guided facilitation rather than passive reception of information. To reinforce learning, participants produced session summaries in the form of wall newspapers at the end of each session.\u003c/p\u003e \u003cp\u003eThe control group received the same nutrition education content, delivered over the same duration and frequency, but through traditional teacher-led lectures consistent with routine classroom instruction. These sessions relied primarily on verbal explanations and textbook-based teaching, without structured peer interaction, group tasks, or collaborative activities. All sessions were facilitated by trained nutritionists and dietitians using standardized educational materials, including posters, leaflets, pamphlets, PowerPoint presentations, and short educational videos. The nutrition education package was developed by the research team and reviewed by academic and professional experts in nutrition prior to implementation. Intervention fidelity, participant engagement, and adherence to the delivery protocol were monitored throughout the intervention period using structured observational checklists. To minimize contamination, intervention and control participants were drawn from different schools. Follow-up data were collected four weeks after completion of the eight-week intervention using the same instruments administered at baseline to assess changes in diet quality and nutrition knowledge attributable to the intervention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Nutrition Education Guide\u003c/h2\u003e \u003cp\u003e A structured, curriculum-based Nutrition Education Guide developed specifically for this study guided the nutrition education intervention. The guide was designed to ensure content validity, logical sequencing, and consistency across sessions. It was adapted from authoritative national and international nutrition education frameworks, including the Food-Based Dietary Guidelines for Nigeria, MyPlate, Kenya\u0026rsquo;s National Dietary and Physical Activity Guidelines, and established clinical nutrition texts [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These sources were selected to ensure scientific rigor, cultural relevance, and alignment with global recommendations for adolescent nutrition.\u003c/p\u003e \u003cp\u003eThe guide was structured into eight progressive lessons, each building on the previous session to promote cumulative learning and reinforcement of key concepts. Initial sessions introduced the importance of healthy eating during adolescence, emphasizing links between diet, growth and development, academic performance, mental well-being, and long-term risk of diet-related non-communicable diseases. Subsequent lessons focused on understanding food groups, nutrient functions, and the principles of constructing a balanced diet using visual dietary models, enabling students to translate abstract nutrition concepts into practical meal choices.\u003c/p\u003e \u003cp\u003eMid-intervention sessions addressed applied dietary skills, including portion control, meal planning, interpretation of nutrition labels, and selection of healthier snack options. These components were intended to strengthen food literacy and empower adolescents to make informed choices within real-world food environments. Additional sessions emphasized hydration and healthy beverage choices, highlighting the role of water and the health risks associated with sugar-sweetened beverages. The guide also explicitly addressed frequent consumption of fast foods, discussing their nutritional composition, associated health risks, and practical strategies for making healthier alternatives.\u003c/p\u003e \u003cp\u003eThe final sessions focused on overcoming barriers to healthy eating, including social influences, economic constraints, and limited access to healthy foods. Students were encouraged to identify context-specific challenges and collaboratively develop feasible solutions, reinforcing problem-solving skills and personal agency. Throughout the curriculum, learning was supported using interactive discussions, visual aids, practical demonstrations, and scenario-based activities, which were particularly suited to the collaborative learning approach adopted in the intervention.\u003c/p\u003e \u003cp\u003eOverall, the Nutrition Education Guide provided a comprehensive yet age-appropriate coverage of adolescent nutrition, integrating knowledge acquisition with practical skill development. Its structured progression, alignment with evidence-based dietary guidelines, and incorporation of participatory learning strategies made it adequate and appropriate for achieving the intervention objectives, particularly in improving diet quality and nutrition knowledge among in-school adolescents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Data Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS version 27 (IBM Corp., Armonk, NY, USA). Categorical socio-demographic and socio-economic variables were summarized as frequencies and percentages, while continuous variables were presented as means and standard deviations. Baseline comparability between intervention and control groups was assessed using the Chi-square test, with Fisher\u0026rsquo;s exact test applied where expected cell counts were \u0026lt;\u0026thinsp;5. Normality was assessed using the Shapiro\u0026ndash;Wilk test. Diet quality indicators and nutrition knowledge scores were analyzed using paired t-tests or Wilcoxon signed-rank tests for within-group comparisons, and independent samples t-tests or Mann\u0026ndash;Whitney U tests for between-group comparisons of change scores, as appropriate. Results were presented in tables and figures. To control for baseline differences, analysis of covariance (ANCOVA) was conducted to examine post-intervention group differences while adjusting for baseline values and relevant covariates, including parental income, occupation, educational status, family size, birth order, and baseline nutrition knowledge. ANCOVA assumptions were verified and met. Partial eta squared (ηp\u0026sup2;) was reported as an effect size measure. Diet quality indicators were computed using the Diet Quality Questionnaire (DQQ) framework. All tests were two-tailed, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Ethical Considerations and Approval\u003c/h2\u003e \u003cp\u003e Ethical clearance for the study was obtained from the Health Research Ethics Committee of the Federal Medical Centre, Abeokuta (FMCA/470/HREC/17/2024/30). Prior to data collection, detailed information about the study objectives, procedures, potential benefits, and participants\u0026rsquo; rights was communicated to both students and their parents/guardians. Written informed consent was secured from parents or guardians, while informed assent was obtained from all adolescent participants. Participation was voluntary, with the right to withdraw at any stage without any consequences. Confidentiality was strictly maintained with anonymized codes instead of personal identifiers, and access to data was restricted to the research team only. The study involved minimal to no risk and was conducted in accordance with internationally accepted ethical standards for research involving human participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sociodemographic and economic characteristics of participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the sociodemographic and economic characteristics of the participants overall and by study group. The mean age of participants was 14.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88 years, with a significant difference in age distribution between the intervention and control groups (P\u0026thinsp;=\u0026thinsp;0.001), driven by a higher proportion of younger adolescents (ages 13\u0026ndash;14) in the control group and a greater concentration of ages 15\u0026ndash;17 in the intervention group. Gender distribution was comparable between groups, with no significant difference observed (P\u0026thinsp;=\u0026thinsp;0.117). Ethnic composition was predominantly Yoruba across both groups, with no significant between-group difference (P\u0026thinsp;=\u0026thinsp;0.542). Household position differed significantly between groups (P\u0026thinsp;=\u0026thinsp;0.001), with first-born participants more common in the intervention group and last-born participants more frequent in the control group. Class level distribution did not differ significantly between groups (P\u0026thinsp;=\u0026thinsp;0.074).\u003c/p\u003e \u003cp\u003eParental educational attainment showed significant group differences. A higher proportion of fathers and mothers in the intervention group had tertiary education compared with the control group (father: P\u0026thinsp;=\u0026thinsp;0.001; mother: P\u0026thinsp;=\u0026thinsp;0.003). Similarly, fathers\u0026rsquo; occupation differed significantly between groups (P\u0026thinsp;=\u0026thinsp;0.003), with administrative occupations more prevalent in the intervention group, whereas farming, craft, and related trades were more common in the control group. Mothers\u0026rsquo; occupation also varied significantly (P\u0026thinsp;=\u0026thinsp;0.001), with a higher proportion of professional and administrative occupations in the intervention group and a greater proportion of farming, craft-related work, and unemployment in the control group. Household monthly income differed significantly between groups (P\u0026thinsp;=\u0026thinsp;0.001), with a larger proportion of households earning more than ₦100,000 in the intervention group compared with the control group. Living arrangement did not differ significantly between groups (P\u0026thinsp;=\u0026thinsp;0.098), with the majority of participants residing with both parents. The number of household members differed significantly (P\u0026thinsp;=\u0026thinsp;0.001), with larger household sizes more common in the control group, whereas the number of siblings was comparable between groups (P\u0026thinsp;=\u0026thinsp;0.295).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic and economic characteristics of the Adolescent\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAggregate\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eIntervention Group\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eControl Group\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eN (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP.Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.680\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;S.D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.82\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.453\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoruba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.226\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgbo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Position\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnly born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.323\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110 (40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLast born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.217\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFather Highest Level of Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.099\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216 (78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother Highest Level of Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.099\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFather's Occupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministrator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.679\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer, Craft and Related Trades Workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArmed Forces Occupations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother's Occupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministrator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.577\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer, Craft and Related Trades Workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArmed Forces Occupations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Monthly Income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.311\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20,000\u0026ndash;50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50,001\u0026ndash;100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWho you live with\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth Parent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128 (85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.294\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuardian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Household Member\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.260\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e208 (75.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;S.D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.37\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Siblings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250 (91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.441\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;S.D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.63\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e χ\u0026sup2; Chi-square, \u003csup\u003eb\u003c/sup\u003e Fisher\u0026rsquo;s Exact test, and Asterisk (*) signify statistical significant differences at \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Diet Quality of the Adolescent\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents within-group changes in diet quality indicators from baseline to endline for the intervention and control groups. In the intervention group, dietary diversity score (DDS) increased significantly from a mean of 4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87 at baseline to 5.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77 at endline (mean difference [MD]: 0.68; 95% CI: 0.36, 0.99; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A smaller but statistically significant increase in DDS was also observed in the control group, from 5.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85 to 5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53 (MD: 0.42; 95% CI: 0.06, 0.78; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe NCD-Protect score increased markedly in the intervention group, rising from 2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54 at baseline to 6.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60 at endline (MD: 3.83; 95% CI: 3.48, 4.19; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, the control group showed a smaller increase from 3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18 to 3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36, which was not statistically significant (MD: 0.79; 95% CI: 0.33, 1.27; P\u0026thinsp;=\u0026thinsp;0.361). The NCD-Risk score decreased significantly in the intervention group, declining from 3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19 at baseline to 2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43 at endline (MD: \u0026minus;1.07; 95% CI: \u0026minus;1.49, \u0026minus;\u0026thinsp;0.64; P\u0026thinsp;=\u0026thinsp;0.001). In the control group, the NCD-Risk score remained essentially unchanged between baseline and endline (3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37 vs. 3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07), with a minimal mean difference (MD: 0.40; 95% CI: \u0026minus;0.45, 0.53; P\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e \u003cp\u003eThe Global Dietary Recommendation (GDR) score increased substantially in the intervention group from 8.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78 at baseline to 12.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86 at endline (MD: 4.90; 95% CI: 4.34, 5.46; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In comparison, the control group exhibited a modest increase from 8.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18 to 9.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62, which was not statistically significant (MD: 0.76; 95% CI: 0.03, 1.49; P\u0026thinsp;=\u0026thinsp;0.885).\u003c/p\u003e \u003cp\u003eThe between-group analysis showed that the intervention group experienced significantly greater improvements than the control group in NCD\u0026ndash;protect score (CMD\u0026thinsp;=\u0026thinsp;3.03; 95% CI: 2.46\u0026ndash;3.61; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), NCD\u0026ndash;risk score (CMD\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.11; 95% CI: \u0026minus;1.75 to \u0026minus;\u0026thinsp;0.46; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and global dietary recommendation (GDR) score (CMD\u0026thinsp;=\u0026thinsp;4.14; 95% CI: 3.24\u0026ndash;5.04; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the between-group change in dietary diversity score (DDS) was not statistically significant (CMD\u0026thinsp;=\u0026thinsp;0.26; 95% CI: \u0026minus;0.21\u0026ndash;0.74; p\u0026thinsp;=\u0026thinsp;0.280). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable 2 Diet Quality of the Adolescent\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c11\" namest=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eIntervention Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eControl Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEndline\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI of the difference\u003c/p\u003e \u003cp\u003e(LL-UL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP.Value\u003csup\u003e↕\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEndline\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95% CI of the difference\u003c/p\u003e \u003cp\u003e(LL-UL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP.Value\u003csup\u003e↕\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCMD (95% CI of the DID)\u003c/p\u003e \u003cp\u003eLL-UL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP.Value\u003csup\u003e\u0026darr;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDS \u003csup\u003e(0\u0026ndash;10)a\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.92\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.60\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.32\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.74\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u0026ndash;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.26 (-0.213\u0026ndash;0.735)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCD-Protect Score \u003csup\u003e(0\u0026ndash;9)b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.40\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.32\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.48\u0026ndash;4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.16\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.96\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.33\u0026ndash;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.03 (2.459\u0026ndash;3.611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCD-Risk Score \u003csup\u003e(0\u0026ndash;9)c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.49\u0026ndash;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.77\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.81\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.45\u0026ndash;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.11 (-1.748 - -4.655)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDR Score \u003csup\u003e(0\u0026ndash;18)d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.07\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.97\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.34\u0026ndash;5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.40\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.15\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.14 (3.242\u0026ndash;5.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eStatistical Test: \u003csup\u003e↕\u003c/sup\u003ePaired T Test, \u003csup\u003e\u0026darr;\u003c/sup\u003eIndependent Test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSD, Standard Deviation; MD, Mean Difference; CI, Confidence Interval; LL, Lower Limit; UL, Upper Limit; N, Frequency; DID, Difference in difference; CMD, Change mean difference; GDR, global dietary recommendations; NCD, non-communicable disease.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Dietary diversity Score (DDS) includes ten food groups: (1) grains, white roots and tuber, and plantains; (2) pulses (beans, peas and lentils); (3) nuts and seeds; (4) dairy; (5) meat, poultry and fish; (6) eggs; (7) dark green leafy vegetables; (8) other vitamin A-rich fruits and vegetables; (9) other vegetables; (10) other fruits.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e NCD \u0026ndash; protect score measures adherence to global dietary recommendations on foods to consume: (1) whole grains; (2) pulses; (3) nuts and seeds; (4) vitamin A-rich orange vegetables; (5) dark green leafy vegetables; (6) other vegetables; (7) vitamin A-rich fruits; (8) citrus; (9) other fruits.\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e NCD \u0026ndash; risk score measures adherence to global dietary recommendations on foods to limit including: (1) soft drinks; (2) baked/grain-based sweets; (3) other sweets; (4) processed meats; (5) unprocessed meat; (6) deep fried food; (7) fast food and instant noodles; (8) packaged ultra-processed salty snacks.\u003c/p\u003e \u003cp\u003e \u003csup\u003ed\u003c/sup\u003e GDR score = (NCD \u0026ndash; Protect \u0026ndash; NCD \u0026ndash; Risk)\u0026thinsp;+\u0026thinsp;9; measures adherence to global dietary recommendations protective against non-communicable diseases.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3\u003c/b\u003e Diet Quality Indicators NCD Protect (+), NCD Risk (-), and Minimum Dietary Diversity for women of participant\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea. present at baseline, a high proportion of participants in the private school intervention group consumed at least one starchy staple (98%) and at least one animal-source food (94%), while consumption of at least one vegetable was reported by 84% and at least one fruit by 31%. At endline, the proportion consuming at least one vegetable increased to 94%, fruit consumption increased to 55%, and intake of pulses, nuts, or seeds increased from 50% to 58%. Pulse consumption specifically increased from 25% at baseline to 47% at endline, and whole-grain consumption increased modestly from 14% to 19%. Consumption of at least one vegetable or fruit declined from 88% at baseline to 58% at endline.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. shows NCD risk indicators, processed meat consumption increased from 23% at baseline to 40% at endline. In contrast, the proportion consuming salty or fried snacks declined from 61% to 29%, deep-fried food from 47% to 40%, sweet foods from 75% to 35%, and soft drinks from 52% to 35%. The proportion reporting zero vegetable or fruit consumption increased from 13% at baseline to 90% at endline.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4\u003c/b\u003e Nutrition Knowledge\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents significant differences in general nutrition knowledge scores among participants, particularly between intervention and control groups. A similar trend was observed in private schools, where the intervention group\u0026rsquo;s mean score significantly increased from 63.64 (SD\u0026thinsp;=\u0026thinsp;10.31) to 73.03 (SD\u0026thinsp;=\u0026thinsp;12.31), with a mean difference of 9.39 (95% CI: 5.486, 13.302; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), confirming the intervention's effectiveness. The control group, however, showed minimal and non-significant improvement, with scores rising from 51.87 (SD\u0026thinsp;=\u0026thinsp;17.55) to 53.48 (SD\u0026thinsp;=\u0026thinsp;17.17), resulting in a mean difference of 1.61 (95% CI: -4.559, 7.785; p\u0026thinsp;=\u0026thinsp;0.606). This highlights the limited impact of nutrition knowledge without targeted intervention. The between-group change mean difference (difference-in-differences) indicated a significantly greater improvement in nutrition knowledge in the intervention group compared with the control group (CMD\u0026thinsp;=\u0026thinsp;7.94; 95% CI: 2.18\u0026ndash;13.69; p\u0026thinsp;=\u0026thinsp;0.007) Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutrition Knowledge of the participant\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM.D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003cp\u003e(LL - UL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP.Value\u003csup\u003e↕\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCMD (95% CI of the DID)\u003c/p\u003e \u003cp\u003eLL-UL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP.Value\u003csup\u003e\u0026darr;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.486\u0026ndash;13.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.94 (2.183\u0026ndash;13.687)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.559\u0026ndash;7.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.78 (3.401\u0026ndash;12.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eStatistical Test: \u003csup\u003e↕\u003c/sup\u003ePaired T Test, \u003csup\u003e\u0026darr;\u003c/sup\u003eIndependent Test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eN, Frequency; M, Mean, SD, Standard Deviation; M.D, Mean Difference; CI, Confidence Interval; LL, Lower Limit; UL, Upper Limit; N, Frequency; DID, Difference in difference; CMD, Change mean difference\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Paired T Test\u003c/p\u003e \u003cp\u003e*Signifies Statistical Significant difference at \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5\u003c/b\u003e ANCOVA of Global Dietary Recommendation (GDR) Score, Diet Diversity (DD) Score, and Nutrition Knowledge (NK) Score by Sociodemographic and Economic Characteristics\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003ea shows the results after adjustment for baseline GDR scores. ANCOVA demonstrated a significant main effect of intervention group on post-intervention GDR scores (\u003cem\u003eF\u003c/em\u003e (1, 225)\u0026thinsp;=\u0026thinsp;77.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), corresponding to a large effect size (partial η\u0026sup2; = 0.26). None of the sociodemographic or economic characteristics including household size, gender, parental occupation, or household income were independently associated with post-intervention GDR scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The overall model was statistically significant (\u003cem\u003eF\u003c/em\u003e (48, 225)\u0026thinsp;=\u0026thinsp;5.69, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and explained approximately 45% of the variance in GDR outcomes (adjusted R\u0026sup2; = 0.45), indicating a robust intervention effect independent of baseline dietary patterns.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, baseline DD score significantly predicted post-intervention DD score (\u003cem\u003eF\u003c/em\u003e (1, 225)\u0026thinsp;=\u0026thinsp;50.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), corresponding to a large effect size (partial η\u0026sup2; = 0.18). Significant main effects were observed for household size (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), household income (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), gender (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), father\u0026rsquo;s occupation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and mother\u0026rsquo;s occupation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038). Although the main effect of intervention group was not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.628), several moderate-to-large interaction effects were identified, including group \u0026times; gender (partial η\u0026sup2; = 0.09) and group \u0026times; father\u0026rsquo;s occupation (partial η\u0026sup2; = 0.26). These findings indicate heterogeneous intervention effects, with improvements in dietary diversity concentrated within specific sociodemographic subgroups. Overall, the DD score model explained 54.3% of the variance in post-intervention dietary diversity (adjusted R\u0026sup2; = 0.54).\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, after adjustment for baseline nutrition knowledge, a significant intervention effect on post-intervention knowledge scores was observed (\u003cem\u003eF\u003c/em\u003e (1, 225)\u0026thinsp;=\u0026thinsp;34.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), corresponding to a large effect size (partial η\u0026sup2; = 0.13). Baseline nutrition knowledge remained a significant covariate (\u003cem\u003eF\u003c/em\u003e (1, 225)\u0026thinsp;=\u0026thinsp;9.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Father\u0026rsquo;s occupation was independently associated with nutrition knowledge outcomes (\u003cem\u003eF\u003c/em\u003e (3, 225)\u0026thinsp;=\u0026thinsp;3.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). In addition, significant interaction effects were observed between intervention group and both father\u0026rsquo;s occupation (\u003cem\u003eF\u003c/em\u003e (2, 225)\u0026thinsp;=\u0026thinsp;5.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and mother\u0026rsquo;s occupation (\u003cem\u003eF\u003c/em\u003e (2, 225)\u0026thinsp;=\u0026thinsp;7.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), suggesting that intervention effectiveness varied across parental occupational strata. The final model accounted for 63.5% of the variance in post-intervention nutrition knowledge scores (adjusted R\u0026sup2; = 0.64).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ea\u003c/b\u003e ANCOVA of Global Dietary Recommendation (GDR) Score by Sociodemographic and Economic Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eηp\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline GDR score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup (intervention vs control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel fit: \u003cem\u003eF\u003c/em\u003e (48, 225)\u0026thinsp;=\u0026thinsp;5.69, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Adjusted R\u0026sup2; = 0.452\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Signifies Statistical Significant difference at \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eb\u003c/b\u003e ANCOVA of Diet Diversity (DD) Score by Sociodemographic and Economic Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eηp\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline DD score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup (intervention vs control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; Father\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome \u0026times; Father\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender \u0026times; Mother\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather\u0026rsquo;s \u0026times; Mother\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender \u0026times; Father\u0026rsquo;s \u0026times; Mother\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel fit: \u003cem\u003eF\u003c/em\u003e (48, 225)\u0026thinsp;=\u0026thinsp;7.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Adjusted R\u0026sup2; = 0.543\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Signifies Statistical Significant difference at \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ec\u003c/b\u003e ANCOVA of Nutrition Knowledge (NK) Score by Sociodemographic and Economic Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eηp\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline NK score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup (intervention vs control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; Father\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; Mother\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather\u0026rsquo;s \u0026times; Mother\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u0026times; Gender \u0026times; Father\u0026rsquo;s occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel fit: \u003cem\u003eF\u003c/em\u003e (48, 225)\u0026thinsp;=\u0026thinsp;10.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Adjusted R\u0026sup2; = 0.635\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Signifies Statistical Significant difference at \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study demonstrates that a school-based collaborative learning\u0026ndash;based nutrition education intervention produced meaningful improvements in multiple dimensions of diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria. Relative to usual didactic instruction, the intervention resulted in significantly higher dietary diversity, greater consumption of NCD-protective foods, improved adherence to Global Dietary Recommendations (GDR), and reduced intake of NCD-risk foods. Together, these findings provide robust evidence that pedagogical approach not merely content matters for improving adolescent diet quality in low- and middle-income settings.\u003c/p\u003e \u003cp\u003eDietary diversity, a widely used proxy for micronutrient adequacy and overall diet quality, improved in both study arms, but gains were consistently greater among adolescents exposed to the collaborative learning intervention. This pattern aligns with evidence that nutrition education enhances adolescents\u0026rsquo; awareness of food groups and supports more varied food choices when learning is interactive and contextually relevant [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similar associations between improved nutrition knowledge and higher dietary diversity have been reported among Nigerian adolescents, reinforcing the role of structured education in promoting food variety [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond diversity, the intervention produced a pronounced increase in the NCD-Protect score, reflecting higher intake of fruits, vegetables, legumes, and whole grains\u0026mdash;foods strongly associated with reduced cardiometabolic risk. In contrast, changes in the control group were modest and non-significant. Systematic reviews consistently show that school-based nutrition education can increase adolescents\u0026rsquo; consumption of fruits and vegetables, particularly when programs emphasize skills, peer engagement, and active learning [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Concurrently, the observed reduction in NCD-Risk scores among intervention participants indicates decreased consumption of unhealthy foods, consistent with prior evidence linking nutrition education to lower intake of sugar-sweetened beverages, fried foods, and energy-dense snacks among youth [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Improvements in the GDR score suggest that the intervention supported broader alignment with holistic dietary recommendations rather than isolated food substitutions. This finding is particularly relevant for adolescent nutrition, as composite indices better capture real-world dietary patterns than single-nutrient outcomes. Meta-analytic evidence indicates that comprehensive school-based nutrition programs can improve overall dietary adherence, even when effects on individual food groups vary [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt baseline, adolescents particularly in private schools exhibited high consumption of staples and animal-source foods but suboptimal intake of fruits and vegetables, a pattern widely observed among adolescents in LMICs [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. By endline, marked increases were observed in vegetable and fruit consumption, as well as in pulses, nuts, seeds, and whole grains, all of which contributed to improved NCD-Protect scores. These shifts indicate that the intervention effectively promoted protective food choices central to long-term NCD prevention. Improved dietary diversity and protective food intake are consistent with evidence that education-driven behavior change strategies can enhance micronutrient adequacy by increasing food group variety [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Although the Minimum Dietary Diversity for Women (MDD-W) was originally developed for women aged 15\u0026ndash;49 years, its conceptual basis that consumption of multiple food groups signals greater micronutrient adequacy is equally relevant for adolescents [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Thus, observed improvements in dietary diversity likely reflect broader gains in nutrient adequacy.\u003c/p\u003e \u003cp\u003eMixed trends were observed for specific NCD-risk foods. While intake of salty snacks, sweet foods, fried foods, and sugar-sweetened beverages declined substantially and this consistent with prior school-based intervention evidence [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. processed meat consumption increased. This likely reflects broader food environment influences, including availability, marketing, and social norms, which may not be fully addressed by education alone. Global evidence increasingly implicates ultra-processed foods, including processed meats, in declining diet quality and rising adolescent obesity and NCD risk [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Some inconsistencies in reported zero fruit or vegetable consumption across time points may reflect measurement nuances or recall variability rather than true behavioral reversal. Such discrepancies highlight the complexity of dietary assessment in adolescents and reinforce the value of validated, food-group\u0026ndash;based indicators such as those embedded in the DQQ framework [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe intervention produced a substantial and statistically significant improvement in nutrition knowledge, with a mean increase of over nine points in the intervention group, while changes in the control group were negligible. These findings align with growing evidence that school-based nutrition education, particularly when interactive and participatory, effectively enhances adolescents\u0026rsquo; nutrition knowledge [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCollaborative learning likely amplified knowledge gains through peer discussion, shared problem-solving, and active engagement, mechanisms known to improve comprehension and retention beyond traditional lecture-based instruction [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Umbrella reviews of school-based healthy eating interventions consistently report improvements in nutrition knowledge and attitudes when education is embedded within participatory learning environments [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough knowledge gains do not always translate directly into sustained behavior change, nutrition knowledge is a necessary precursor for informed food choice and dietary self-regulation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Evidence from sub-Saharan Africa further supports the generalizability of these findings, with structured school-based nutrition education improving knowledge among children and adolescents across diverse contexts [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. After adjustment for baseline values and covariates, the intervention exerted a strong independent effect on post-intervention GDR scores, with a large effect size, indicating that collaborative learning\u0026ndash;based nutrition education robustly improved overall dietary adherence irrespective of sociodemographic background. Similar findings have been reported in systematic reviews demonstrating favorable changes in composite diet quality indices following school-based interventions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The absence of significant sociodemographic effects on GDR outcomes suggests that school-based nutrition education may act as an equalizer, mitigating disparities in diet quality linked to household income or parental occupation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In contrast, dietary diversity outcomes were more strongly influenced by baseline diet and socioeconomic factors, underscoring the role of food access and household resources in shaping diet variety [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. For nutrition knowledge, the intervention remained highly effective after adjustment, with baseline knowledge and parental occupation emerging as important predictors. These findings echo prior research showing that parental socioeconomic status shapes adolescents\u0026rsquo; exposure to, and application of, nutrition information [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study demonstrates that collaborative learning\u0026ndash;based nutrition education grounded in the Health Belief Model can meaningfully improve diet quality and nutrition knowledge among in-school adolescents. For public health nutrition practice, the findings support the integration of interactive, school-based nutrition education into routine adolescent health services as a preventive strategy against poor diet quality and future non-communicable diseases. The intervention\u0026rsquo;s effectiveness independent of baseline dietary patterns suggests its applicability across diverse adolescent populations. From a policy perspective, the results align with Nigeria\u0026rsquo;s National School Health Policy, reinforcing schools as critical platforms for health promotion. The differential effects observed across sociodemographic groups highlight the need for context-sensitive implementation, with adaptations that account for household socioeconomic conditions. Scaling such interventions through curriculum integration and collaboration between health and education sectors may strengthen adolescent nutrition outcomes in resource-limited settings.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Strengths, Limitations, and Potential Biases\u003c/h2\u003e \u003cp\u003eKey strengths of this study include the quasi-experimental pre\u0026ndash;post design with a control group and the use of ANCOVA to adjust for baseline outcomes and sociodemographic factors, enhancing internal validity. The application of the Global Diet Quality Questionnaire, a standardized and internationally comparable tool, strengthened measurement rigor and alignment with global nutrition frameworks. The theory-driven, participatory intervention further enhanced relevance and engagement. However, limitations should be noted. The non-randomized design may allow residual confounding despite statistical adjustment. Dietary intake and nutrition knowledge were self-reported and may be subject to recall or social desirability bias. The relatively short follow-up limits inference on the sustainability of effects, and the restriction to selected schools in one local government area may limit generalizability to other settings.\u003c/p\u003e \u003cp\u003eInformation bias may have arisen from self-reported dietary intake and knowledge, particularly at endline when intervention participants may have been more aware of recommended behaviors. Performance bias is possible, as the intervention group received greater interaction and engagement through collaborative learning compared with controls. Selection bias related to school or group allocation cannot be fully excluded, although baseline adjustment reduced this risk. The observed interaction effects by parental occupation and household characteristics suggest differential responsiveness, indicating potential effect modification rather than uniform intervention impact. These biases should be considered when interpreting the magnitude of observed effects and in planning future implementations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Implications for Future Research\u003c/h2\u003e \u003cp\u003eFuture studies should assess the long-term sustainability of improvements in diet quality and nutrition knowledge through extended follow-up periods. Randomized or cluster-randomized designs across multiple regions would strengthen causal inference and generalizability. Mixed-methods research could elucidate contextual mechanisms underlying differential effects by socioeconomic status, while implementation research is needed to evaluate scalability, cost-effectiveness, and integration within national school health systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides evidence that a collaborative learning\u0026ndash;based nutrition education intervention can significantly improve overall diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria. Adolescents exposed to the intervention demonstrated greater adherence to global dietary recommendations, increased consumption of NCD-protective foods, reduced intake of NCD-risk foods, and substantially higher nutrition knowledge compared with peers receiving usual didactic instruction. These findings underscore the importance of pedagogical approach in shaping adolescents\u0026rsquo; dietary behaviours and nutrition literacy, particularly in settings undergoing rapid dietary transition. By combining standardized diet quality indicators with nutrition knowledge outcomes, this study offers mechanistic insight into how interactive, learner-centred education may facilitate healthier dietary patterns during adolescence a critical window for long-term NCD prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDare D. Ad\u0026eacute;miluyi, Ojo-Adalumo A. Rhoda, Esther D.Olubiyi, Akinade E. Ogunniyi.\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally to the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data produced are available within the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical clearance for the study was obtained from the Health Research Ethics Committee of the Federal Medical Centre, Abeokuta (FMCA/470/HREC/17/2024/30). Prior to data collection, detailed information about the study objectives, procedures, potential benefits, and participants\u0026rsquo; rights was communicated to both students and their parents/guardians. Written informed consent was secured from parents or guardians, while informed assent was obtained from all adolescent participants. Participation was voluntary, with the right to withdraw at any stage without any consequences. Confidentiality was strictly maintained with anonymized codes instead of personal identifiers, and access to data was restricted to the research team only. The study involved minimal to no risk and was conducted in accordance with internationally accepted ethical standards for research involving human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the manuscript\u0026rsquo;s final version and approved it for submission for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNorris SA, Frongillo EA, Black MM, Dong Y, Fall C, Lampl M, et al. Nutrition in adolescent growth and development. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.journalajfrn.com/index.php/AJFRN/article/view/103\u003c/span\u003e\u003cspan address=\"https://www.journalajfrn.com/index.php/AJFRN/article/view/103\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanlier N, Kocaay F, Kocabas S, Ayyildiz P. The effect of sociodemographic and anthropometric variables on nutritional knowledge and nutrition literacy. Foods. 2024;13(2):346. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/foods13020346\u003c/span\u003e\u003cspan address=\"10.3390/foods13020346\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"adolescents, diet quality, nutrition education, nutrition knowledge, non-communicable diseases, school-based intervention","lastPublishedDoi":"10.21203/rs.3.rs-8560558/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8560558/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePoor diet quality and inadequate nutrition knowledge during adolescence contribute to micronutrient deficiencies and increasing non-communicable disease (NCD) risk in low- and middle-income countries. Evidence remains limited on whether collaborative learning\u0026ndash;based nutrition education improves objectively measured diet quality among adolescents in sub-Saharan Africa. This study evaluated the effect of a collaborative learning\u0026ndash;based nutrition education intervention on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA quasi-experimental pretest\u0026ndash;posttest control group design was employed among 274 adolescents aged 13\u0026ndash;19 years enrolled in private secondary schools. Participants were assigned to either a collaborative learning\u0026ndash;based nutrition education intervention or usual didactic instruction over eight weeks. Diet quality was assessed using the Diet Quality Questionnaire (DQQ), generating Dietary Diversity Score (DDS), NCD-Protect, NCD-Risk, and Global Dietary Recommendations (GDR) scores. Nutrition knowledge was assessed using a validated questionnaire. Within- and between-group changes were analyzed using paired and independent tests, and analysis of covariance (ANCOVA) adjusted for baseline values and sociodemographic factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared with controls, the intervention group showed significantly greater improvements in NCD-Protect score (Δ\u0026thinsp;=\u0026thinsp;3.03; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), NCD-Risk score (Δ=\u0026minus;1.11; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and GDR score (Δ\u0026thinsp;=\u0026thinsp;4.14; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Improvements in DDS were observed in both groups, with no significant between-group difference. Nutrition knowledge increased significantly in the intervention group (Δ\u0026thinsp;=\u0026thinsp;9.39; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ANCOVA confirmed a strong independent intervention effect on GDR (ηp\u0026sup2;=0.26) and nutrition knowledge (ηp\u0026sup2;=0.13).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCollaborative learning\u0026ndash;based nutrition education significantly improves diet quality and nutrition knowledge among adolescents and represents a promising school-based strategy for NCD prevention in settings undergoing dietary transition.\u003c/p\u003e","manuscriptTitle":"Effect of a collaborative learning–based nutrition education intervention on diet quality and nutrition knowledge among in-school adolescents in Ogun State, Nigeria: a quasi-experimental study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 14:24:09","doi":"10.21203/rs.3.rs-8560558/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T05:29:03+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"88038341486299532919094351946655332427","date":"2026-01-25T05:08:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T23:26:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6752330451168090698323644725421810778","date":"2026-01-22T08:01:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123901872034693003191550556889245647880","date":"2026-01-21T19:39:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-21T17:45:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150425753859479326488025711883508609475","date":"2026-01-21T15:41:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-21T09:48:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57330476836647256693454611374801313055","date":"2026-01-21T09:12:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T08:15:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-18T12:14:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T05:49:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-13T05:49:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-01-09T11:18:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dc0dc831-d617-462c-a9e7-e463924ff61e","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:34:35+00:00","versionOfRecord":{"articleIdentity":"rs-8560558","link":"https://doi.org/10.1186/s12982-026-01909-y","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2026-04-28 15:57:43","publishedOnDateReadable":"April 28th, 2026"},"versionCreatedAt":"2026-01-22 14:24:09","video":"","vorDoi":"10.1186/s12982-026-01909-y","vorDoiUrl":"https://doi.org/10.1186/s12982-026-01909-y","workflowStages":[]},"version":"v1","identity":"rs-8560558","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8560558","identity":"rs-8560558","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}