Physical activity and energy intake in adolescents: Gender differences and dietary pattern clustering in an ongoing nutrition transition

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Understanding the relationship between physical activity and energy intake is essential for promoting health and preventing early onset of metabolic risk. Objective This study examines gender differences in the associations between physical activity and energy intake among adolescents, alongside dietary pattern clustering during an ongoing nutrition transition. Methods A cross-sectional study was conducted among 447 plausible respondents aged 13–15 years. Energy intake was assessed using a validated FFQ, physical activity using the PAQ-A, and dietary patterns identified through principal component analysis (PCA). Statistical analyses included ANOVA, correlation, and regression. Results Energy intake differed significantly by physical activity level (p = 0.002) and gender (p < 0.001), but not by area (p = 0.73). PCA identified three dietary patterns explaining 65% of variance: plant-based traditional, mixed, and processed/junk-food. Their coexistence reflects an ongoing nutrition transition among adolescents. The interaction between gender and physical activity was significant (p = 0.034), indicating stronger intake-activity linkage among males. Conclusions Energy consumption among adolescents is primarily influenced by physical activity and gender, reflecting behavioral rather than geographic determinants. The coexistence of traditional and modern dietary patterns indicates an ongoing nutrition transition requiring gender-responsive and school-based health education strategies integrated into national adolescent nutrition programs. physical activity adolescents energy intake gender differences dietary patterns nutrition transition clustering Figures Figure 1 Figure 2 Figure 3 Introduction Adolescence is a pivotal stage in human development characterized by rapid somatic growth and the consolidation of behavioral patterns that often carry into adulthood. Nutritionally, adolescents have high energy and nutrient requirements to support growth and maturation, while at the same time they are unusually sensitive to environmental and social influences that shape food choices and physical activity. 1 The global phenomenon known as the nutrition transition—the population-level shift from traditional diets toward increased consumption of processed, energy-dense foods and sugars—has been documented across low- and middle-income countries (LMICs) and is tightly linked to concurrent declines in physical activity and increases in sedentary behavior. 2 , 3 These shifts create a double burden in many settings where undernutrition persists alongside rising overweight and diet-related non-communicable diseases (NCDs). 4 Changes in adolescent diet and activity are not only driven by macroeconomic forces but also by local food environments, school canteen offerings, advertising, and social norms, which can be understood through behavioral theories such as the Theory of Planned Behavior (TPB) and the Prototype Willingness Model (PWM). 5 , 6 TPB posits that behaviors like physical activity and healthy eating are influenced by attitudes, subjective norms (e.g., peer and family expectations), and perceived behavioral control (e.g., confidence in overcoming barriers). PWM extends this by incorporating social reactive elements, such as prototype favorability (positive views of typical active/eaters) and behavioral willingness (openness to spontaneous actions), which are particularly relevant for adolescents' impulsive decisions around diet and activity. Several recent studies in Southeast Asia demonstrate that adolescents are increasingly exposed to processed snacks and sugar-sweetened beverages, which reduces dietary quality and may narrow traditional urban–rural differences in intake. 7 , 8 These environmental drivers are important because they shape both what adolescents choose to eat and the contexts (e.g., school break times, neighborhood stores) in which snacking occurs. Such convergence of dietary exposures across geographical strata has been hypothesized as a key mechanism behind the homogenization of adolescent diets observed in multiple LMIC contexts. 9 Physical activity (PA) is a crucial, yet sometimes under-emphasized, determinant of energy balance during adolescence. Activity level influences total energy expenditure and may also be associated with patterns of energy intake: active adolescents can, in many settings, consume more calories to meet higher energy demands, while sedentary adolescents may adopt different snack-based eating patterns. 10 Empirical studies find modest but consistent positive associations between PA scores and total energy intake among youth — associations that are often statistically significant but explain only a small fraction of interindividual variation in intake (e.g., r ≈ 0.15–0.30). 11 This implies that while PA contributes to energy intake, behavioral, cultural and environmental factors (peer norms, body image pressures, accessibility of food) also have substantial influence. Within the school setting, interventions that combine physical activity promotion and nutritional education have been shown in several contexts to improve health behaviors among children and adolescents (systematic and scoping reviews highlight the promise and limitations of combined PA–nutrition programs). 12 These findings align closely with the practical policy implications of the current study, which examines how PA, gender and area interact in shaping energy consumption among adolescents. Despite these global and regional insights, there is a gap in the literature regarding the joint influence of physical activity, gender, and area of residence (urban, transitional, rural) on adolescent energy intake in Indonesia. Gender differences are particularly relevant: boys typically show higher energy intakes and higher physical activity levels, while girls may exhibit greater dietary restraint related to body image concerns—factors that complicate straightforward predictions of intake based on activity alone. 13 Guided by TPB and PWM, we hypothesize that perceived behavioral control and subjective norms (e.g., gender-specific social expectations) moderate the PA-intake relationship, with stronger links in males due to favorable prototypes of active boys. Additionally, the “transitional” areas (peri-urban or semi-urban) often combine features of both rural and urban food environments and may therefore be critical zones for observing early stages of dietary convergence. Understanding these joint influences is necessary to design school-level and community interventions that are gender-sensitive and activity-aligned. Accordingly, the present study examines the association between physical activity (measured via PAQ-A) and total energy intake among adolescents, how this association differs by gender and across urban, transitional, and rural settings, and the underlying dietary patterns (via PCA) that may explain observed differences in energy consumption. To our knowledge, no prior Indonesian study has jointly examined physical activity, gender, and area of residence in relation to energy intake and dietary patterns. Methods Study Design and Participants A cross-sectional study was conducted among adolescents aged 13–15 years attending four public junior high schools located in urban, transitional, and rural areas of Bali, Indonesia, from August to November 2025. A total of 692 eligible adolescents completed the questionnaires (all students in selected classes who provided consent). After plausibility screening using the EI/BMR ratio (1.1–2.5 cut-off to exclude under- or over-reporters, following standard nutritional epidemiology methods), 245 were excluded due to implausible energy intake. A total of 447 plausible respondents (137 males, 310 females) were included in the final analysisSample size was calculated based on a power of 0.80 to detect moderate effect sizes (f = 0.25) in ANOVA interactions, assuming alpha = 0.05 and accounting for clustering within schools. This study was approved by the Ethics Committee of the Faculty of Medicine, Universitas Udayana, Bali, Indonesia (Approval No. 2614/UN14.2.2.VII.14/LT/2025). Written informed consent was obtained from the school authorities and parents or guardians of all participating students. The study procedures adhered to the ethical principles of the Declaration of Helsinki and complied with STROBE-nut guidelines for observational nutritional studies (see Additional File 1). Instruments and Measures Energy Intake. Dietary intake was assessed using a validated Food Frequency Questionnaire (FFQ) adapted for Indonesian adolescents. Nutrient values were derived from the Indonesian Food Composition Database (TKPI, Ministry of HealthKemenkes RI, 2019). A recent validation study among Indonesian adolescents found moderate to good validity for a semi-quantitative FFQ to estimate sugar intake. 14 The FFQ recorded frequency and portion size of major food groups over the past month; daily energy intakes (kcal/day) were calculated. Implausible intakes (EI/BMR 2.5) were excluded, following standard methodology for adolescent nutrition surveillance. 15 Physical Activity. Physical activity was measured via the Physical Activity Questionnaire for Adolescents (PAQ-A), a self-administered 7-day recall tool validated in multiple cultural settings (e.g., Spanish adolescents: reliability & validity established). 16 Scores range from 1 (low) to 5 (high); in this study participants were further categorized into three groups – Low (≤ 2.0), Moderate (> 2.0 – ≤ 3.0), and High (> 3.0) based on tertiles. Dietary Pattern. Dietary data were derived from a semi-quantitative food frequency questionnaire (FFQ) that assessed habitual intake of various food items during the past month. To minimize reporting bias, only plausible reporters—participants whose reported energy intake was within a biologically plausible range—were included for this analysis (n = 447). A total of 19 food-frequency items were selected, covering staple foods, plant-based proteins, animal proteins, fruits, vegetables, beverages, and snack foods. Items were chosen based on common Indonesian adolescent diets from prior surveys. 17 Categorical responses (e.g., “every day”, “1–2 times per week”, “never”) were converted to times per day, using a standard conversion scale: ≥ 2 times/day = 2.0, once/day = 1.0, 3–4 times/week = 0.47, 1–2 times/week = 0.20, 1–3 times/month = 0.07, never = 0.00. All variables were standardized (z-scores) prior to analysis. Principal Component Analysis (PCA) was employed to identify underlying dietary patterns based on inter-correlations among food items. Sampling adequacy was verified using the Kaiser–Meyer–Olkin (KMO) test (> 0.6) and Bartlett’s Test of Sphericity (p 1 and the Scree Plot inflection point. To improve interpretability, a Varimax orthogonal rotation was applied, and food items with absolute loadings ≥ 0.30 were used to characterize each dietary pattern. Individual component scores were calculated for each respondent to reflect adherence to each pattern, which were later used in analyses of gender differences, physical activity levels, and total energy intake. 18 Statistical Analysis Descriptive statistics were reported for key variables by gender, activity and area. One-way ANOVA tested differences in energy intake across activity categories and areas. Pearson correlation examined associations between PAQ-A score and energy intake. Linear regression assessed the independent effect of physical activity score (continuous) on energy intake, controlling for gender and area. A three-way factorial ANOVA (Area × Activity × Gender) evaluated interaction effects. PCA results were presented with factor loadings and interpreted patterns. Statistical significance was set at p < 0.05. Analyses were performed using SPSS 26.0 (IBM Corp., Armonk, NY). Results Participant Characteristics A total of 692 adolescents were recruited and completed the questionnaires. After excluding 245 due to implausible energy intake (EI/BMR 2.5, indicating under- or over-reporting), 447 plausible respondents (137 males and 310 females) aged 13–15 years were included in the analysis (see Figure [nomor baru] for participant flow diagram). The mean age of participants was 13.8 ± 0.5 years, with no significant age difference between males and females (p > 0.05). The mean physical activity score (PAQ-A) for the total sample was 2.46 ± 0.63, indicating predominantly moderate levels of habitual activity. Males demonstrated significantly higher activity levels (2.61 ± 0.62) compared to females (2.39 ± 0.60; t(445) = 3.52, p < 0.001). The mean daily energy intake of all participants was 1897 ± 443 kcal/day, with males reporting higher intakes (2056 ± 463 kcal/day) than females (1799 ± 402 kcal/day; p < 0.001). No significant differences were found across areas (urban, transitional, rural; p = 0.73), suggesting relative homogeneity in overall caloric intake across geographic locations. exclusions for implausible energy intake. Table 1 Descriptive Characteristics of Participants Variable Male Female Total n 137 310 447 Age (years) 13.8 ± 0.6 13.9 ± 0.5 13.8 ± 0.5 PAQ-A score 2.61 ± 0.62 2.39 ± 0.60 2.46 ± 0.63 Energy Intake (kcal/day) 2056 ± 463 1799 ± 402 1897 ± 443 Energy Intake by Gender and Area Across all areas, males consistently reported higher energy consumption than females, with mean differences ranging from 200 to 300 kcal/day. Among male students, energy intake was slightly higher in transitional areas (2086 ± 511 kcal/day) compared to urban (2069 ± 387 kcal/day) and rural (1988 ± 486 kcal/day), although these differences were not statistically significant (F(2,134) = 0.51, p = 0.60) (Table 2 ). Similarly, for female students, mean energy intake did not differ significantly across areas (F(2,302) = 0.40, p = 0.67), with the highest average intake observed in urban schools (1818 ± 432 kcal/day). These findings indicate that gender is a stronger determinant of energy intake than area of residence, and that environmental differences between rural and urban schools do not substantially affect total caloric intake. Table 2 Energy Intake by Area and Gender Gender Area n Mean ± SD (kcal/day) Male Urban 43 2069 ± 387 Transitional 60 2086 ± 511 Rural 34 1988 ± 486 Female Urban 88 1818 ± 432 Transitional 142 1778 ± 373 Rural 80 1820 ± 423 Energy Adequacy As shown in Table 3 , the majority of respondents (59.1%) fell into the low adequacy category, 24.2% reported adequate intake, and 16.7% exceeded recommendations. A clear gradient was observed across areas: urban students had the highest proportion of adequate intake (32.1%), while rural students showed the greatest proportion of insufficient intake (66.1%). Although gender differences were not pronounced in adequacy distribution, male participants tended to have slightly higher proportions of both adequate and excess energy intake compared to females, aligning with their higher mean caloric consumption. These results suggest that while mean energy intake does not differ significantly by area, a larger proportion of rural adolescents experience suboptimal caloric adequacy, potentially reflecting lower dietary diversity or meal frequency. Table 3 Energy Adequacy by Area Area Low ( 110%) Rural 66.1 15.6 18.3 Transitional 58.9 23.8 17.3 Urban 53.4 32.1 14.5 Relationship Between Physical Activity and Energy Intake Table 4 describes the relationship between energy intake and physical activity level. A clear upward trend was observed: adolescents classified in the high activity category reported the highest mean energy intake (2051.8 ± 523.8 kcal/day), followed by moderate (1868.3 ± 438.6 kcal/day) and low activity groups (1839.9 ± 404.1 kcal/day). ANOVA results confirmed significant differences across activity levels (F(2,444) = 6.23, p = 0.002). Post-hoc comparisons (Tukey HSD) revealed that the high vs. low activity groups differed significantly (p < 0.01), while moderate and low groups did not. This indicates that students with higher physical activity levels tend to consume more energy, consistent with physiological expectations of energy balance. Table 4 Energy Intake by Physical Activity Category Physical Activity Level n Mean ± SD (kcal/day) F p-value Low 277 1839.9 ± 404.1 6.23 0.002 Moderate 107 1868.3 ± 438.6 High 63 2051.8 ± 523.8 Regression Analysis Between PAQ-A Score and Energy Intake To further examine the relationship between activity and energy intake, a linear regression analysis was performed using total energy intake as the dependent variable and PAQ-A score as the predictor. Results showed a significant positive association (β = +93.3, SE = 25.7, t = 3.64, p < 0.001). The model explained 2.9% of the variance in total energy intake (R² = 0.029), indicating a modest yet statistically significant effect. In practical terms, each one-point increase in physical activity score corresponded to an average increase of 93 kcal/day in total energy consumption. Although the coefficient of determination was low, this finding supports the existence of a meaningful behavioral linkage between activity and energy intake. Table 5 Linear Regression of Physical Activity and Energy Intake Predictor β Std. Error t p-value Constant 1695.6 53.6 31.7 < 0.001 PAQ-A score 93.3 25.7 3.64 < 0.001 Interaction Effects of Area, Physical Activity and Gender The three-way ANOVA results (Table 6 ) revealed that physical activity (p = 0.006) and gender (p < 0.001) significantly influenced energy intake, while area (p = 0.73) had no effect. A significant interaction between activity and gender (p = 0.034) indicated that the effect of activity on energy intake was stronger among males. No significant interactions involving area were detected, suggesting that the observed relationships between activity and intake were consistent across urban, transitional, and rural environments. Graphical inspection (Fig. 3 ) confirmed that males exhibited steeper increases in energy intake with rising activity levels, while females showed a flatter trend. Table 6 Three-Way ANOVA Factor F p-value Significance Area 0.31 0.734 ns Activity 5.12 0.006 ✓ Gender 27.67 < 0.001 ✓✓ Activity × Gender 3.41 0.034 ✓ Dietary Patterns Identified by PCA Three principal dietary patterns were identified, together explaining approximately 65% of the total variance in food frequency data. The rotated component loadings are presented in Table 7 . Pattern 1 (Plant-based Traditional): characterized by higher consumption of tempeh, tofu, and fish, reflecting a traditional Indonesian diet rich in local plant-based protein sources. Pattern 2 (Mixed): showed no dominant food items, likely representing a transitional eating behavior between traditional and modern foods. Pattern 3 (Processed/Junk-food): defined by frequent instant noodle consumption and lower rice intake, indicating a shift toward convenience-oriented, ultra-processed foods. The cumulative variance and scree plot confirmed adequate factor stability. 19 , 20 Table 7 Varimax-rotated loadings of food items on three dietary patterns Food item Pattern 1 Pattern 2 Pattern 3 Tempeh 0.43 – – Tofu 0.43 – – Fish 0.31 – – Instant noodles – – 0.55 Rice – – 0.51 Pattern Scores and Associations PCA scores were computed for each respondent and compared across gender and physical activity categories. Subgroup analyses showed that adolescents with moderate-to-high physical activity had higher scores for the Plant-based Traditional pattern (p ≈ 0.055), while the Processed/Junk-food pattern was more prevalent in low-activity groups (p = 0.502). No significant gender differences in pattern adherence were found, but trends suggested females favored traditional patterns. 21 Correlation analyses (Spearman’s ρ) between dietary pattern scores and total energy intake revealed no statistically significant relationships (p > 0.05 for all patterns), although a weak positive trend was observed for the Processed/Junk-food pattern (Table 8 ). Table 8 Associations of dietary pattern scores with total energy intake and physical activity Pattern Correlation (ρ) p -value ANOVA F p -value Plant-based Traditional Weak (ρ 0.05 2.92 0.055 Mixed None > 0.05 0.89 0.411 Processed/Junk-food Weak (ρ 0.05 0.69 0.502 In particular, the Plant-based Traditional pattern appears consistent with more active lifestyles and may represent a protective dietary habit, while the Processed/Junk-food pattern reflects modernized, low-activity consumption behavior increasingly observed in adolescents. Dietary patterns are converging across areas, with coexistence of traditional and modern snack-based diets reflecting an ongoing nutrition transition. 22 Discussion The present study contributes to the evolving understanding of adolescent energy balance by examining how physical activity, gender and area of residence relate to energy intake among adolescents — and by exploring underlying dietary patterns. The findings highlight that behavior rather than geography appears to dominate energy intake, and they provide empirical support for the notion of dietary convergence in a country undergoing nutrition transition. Physical Activity, Gender and Energy Intake Our results show that adolescents with higher physical activity scores consumed significantly more energy than their less active peers, and that male adolescents consumed more energy than females. This pattern aligns with the physiological energy-balance principle: increased expenditure via physical activity typically necessitates greater intake. For instance, in the HELENA and EYHS studies, vigorous and moderate physical activity were positively associated with higher energy intake in adolescents. 23 More recently, a systematic review found that acute physical activity in children and adolescents leads to modest increases in caloric intake, though the effect size remains small. 11 In our own data the regression model explained approximately 3% of the variance in energy intake (R² ≈ 0.03), which is consistent with previous literature suggesting that physical activity is only one of many determinants of energy intake. 24 The stronger effect of physical activity on energy intake among males (significant Activity × Gender interaction) suggests gender-specific pathways, potentially explained by TPB's perceived behavioral control (e.g., boys perceiving fewer barriers to active lifestyles) and PWM's prototype favorability (e.g., positive social reactions to 'active boys' in Indonesian culture, leading to greater willingness for energy-matching intake). 25 This aligns with evidence that adolescent boys tend to eat more and be more active than girls, amplifying the intake-activity link. 26 , 27 Lack of Area Effect and Dietary Convergence A notable finding is the lack of a significant difference in energy intake across areas (urban, transitional, rural). On the surface, this might appear counterintuitive given the presumed differences in food environment and access between urban and rural schools. However, this result may indicate behavioral and dietary convergence across regions. In other words, the rural and transitional school settings in our study may already mirror the urban food and snack environments — possibly via school canteens, packaged snack availability, or broader penetration of processed foods. This interpretation is supported by recent research in Indonesia. For example, Nurhasan et al. found evidence of declining diet quality and increasing consumption of processed and ultra-processed foods across urban, rural and forested areas, indicating the nutrition transition is pervasive and not confined to urban zones. 28 Furthermore, expert qualitative work has highlighted that modernization, retail penetration and changing food norms are driving convergence of diets in Indonesia. 29 From a policy lens, this suggests that traditional geographic targeting (i.e., “rural vs urban”) may be less effective than focusing on behavioral and environmental interventions that cut across regions. 30 Dietary Patterns and Their Relationship with Physical Activity, Gender, and Energy Intake The identification of three distinct dietary patterns—Plant-based Traditional, Mixed, and Processed/Junk-food—offers insight into the coexistence of traditional and modernized eating habits among adolescents. The Plant-based Traditional pattern, characterized by higher consumption of tempeh, tofu, and fish, likely reflects adherence to culturally embedded dietary habits. In contrast, the Processed/Junk-food pattern, marked by frequent instant noodle consumption and reduced rice intake, illustrates an emerging shift toward convenience-oriented, energy-dense foods. 31 A noteworthy finding from this study is the trend toward higher adherence to the Plant-based Traditional pattern among adolescents with moderate-to-high physical activity levels (p ≈ 0.055). This suggests that students who are more physically active tend to maintain healthier, locally traditional diets, a relationship supported by evidence that physical activity is often positively associated with prudent or health-conscious eating behaviors. 21 , 32 These results align with studies indicating that active adolescents are more likely to consume fruits, vegetables, and home-prepared meals rather than processed snacks or fast foods. 33 Gender-related tendencies may also play a role in shaping these patterns, consistent with TPB's subjective norms (e.g., girls facing stronger norms for dietary restraint). 34 Previous research has shown that female adolescents are more likely to adopt health-oriented dietary patterns, whereas males often consume more energy-dense and processed foods. 35 Although gender differences were not directly tested within the PCA, it is plausible that females contributed more strongly to the Plant-based Traditional pattern, consistent with behavioral evidence in similar age groups. However, this was not directly tested. In the broader context of adolescent nutrition, these behavioral distinctions are influenced not only by biological factors but also by social and environmental exposures—including peer norms, body image concerns, and school food availability. 36 Despite identifying these patterns, no significant correlations were found between dietary pattern scores and total energy intake. This is consistent with previous findings among adolescents, where self-reported dietary frequency data often fail to reflect true energy intake due to underreporting or variation in portion size. 37 Nevertheless, the weak positive trend observed between the Processed/Junk-food pattern and energy intake may indicate compensatory behaviors, such as higher caloric density from processed foods despite lower meal frequency. This aligns with evidence linking ultra-processed food consumption to greater energy density and poorer diet quality. 38 , 39 Public Health Implications From a public health standpoint, these findings underscore a critical behavioral duality among adolescents: the persistence of a protective, traditional dietary pattern in physically active groups, alongside the rapid emergence of processed, convenience-based dietary habits that may pose long-term metabolic risks. This duality is consistent with patterns observed in other middle-income countries undergoing the nutrition transition, where traditional diets coexist with ultra-processed food consumption. 42 Interventions promoting healthy lifestyles among youth should therefore adopt an integrated behavioral approach, informed by TPB and PWM, such as enhancing perceived behavioral control through skill-building and leveraging positive prototypes via peer-led campaigns. For example, eHealth interventions (e.g., apps tracking PA and diet with gender-tailored feedback) have shown promise in RCTs for improving adolescent behaviors. 43,44 Schools and communities could play a pivotal role by encouraging access to nutritious, culturally relevant foods—such as tempeh and vegetables—while restricting ultra-processed snacks and sugary drinks in the school environment. Given our findings, school-based interventions in Indonesia should emphasize integrated nutrition and physical activity promotion, with sensitivity to gender and activity level, rather than relying solely on “urban vs rural” stratification. For example: Physical education (PE) classes could include nutrition-education modules tailored to activity demands and gender (e.g., ensuring female adolescents are sufficiently active and their energy intake supports growth). The school canteen should limit availability and marketing of high-sugar and high-fat snacks, promoting staple-based lunches and healthy snacks, across all school settings. The Indonesian Government’s new labelling rules for food companies (effective by 2027) are timely and aligned with such needs. 45 These regulatory developments may support future school-based nutrition strategies. Strengths and Limitations Strengths of this study include simultaneous measurement of physical activity, plausibility-adjusted energy intake and dietary patterning across multiple residential contexts, which is rare in Indonesian adolescent nutrition research. Use of validated instruments (PAQ-A, FFQ) enhances comparability with international studies. Nonetheless, important limitations exist. The exclusion rate of 245 from 692 recruited (35%) for implausible energy reporting is common in adolescent self-report studies using FFQ but may introduce selection bias toward 'accurate reporters' (e.g., more conscientious or health-conscious individuals). This rigorous screening enhances data quality by minimizing misreporting bias, though it reduces the effective sample size and may limit generalizability to the broader adolescent population. The cross-sectional design precludes causal inference; for instance, whether higher activity leads to higher intake or vice versa cannot be determined, limiting our ability to infer temporal behavioral pathways. Self-reported FFQ and activity questionnaires may involve bias (e.g., underreporting by females due to social desirability, or overreporting of activity), potentially attenuating associations. We recommend future studies incorporate sensitivity analyses for underreporting and objective measures (e.g., accelerometry for PA). The low variance explained by physical activity suggests unmeasured determinants (meal frequency, snack purchasing behavior, socio-economic status, body image) may play major roles. Additionally, although the area classification was urban/transitional/rural, the study sample was school-based in Bali and may not fully represent national Indonesian adolescent populations (e.g., remote islands or urban Java); findings should be generalized cautiously to Bali contexts, with replication needed for broader applicability. Future Directions Future research should adopt longitudinal designs to track how adolescent energy intake, physical activity and dietary patterns evolve over time, especially as food systems continue to change, allowing for causal inference on behavioral mechanisms. Qualitative research exploring the motivations behind snacking, gendered food behaviors and peer influences would complement quantitative findings. Furthermore, intervention trials integrating physical activity promotion, nutrition education and school-canteen reforms, with outcomes on energy intake and growth/adiposity — are urgently needed in the context. 46 Conclusions This study provides new evidence that among adolescents in Bali, physical activity and gender are the primary determinants of energy intake, whereas geographical area (urban, transitional, rural) plays only a minor role. Male adolescents and those with higher levels of physical activity consumed significantly more energy, reflecting physiological energy balance and behavioral differences between genders. The absence of significant area effects, combined with the identification of both traditional and modern/snacking dietary patterns across all settings, suggests a behavioral and dietary convergence consistent with an ongoing nutrition transition. This convergence implies that adolescents, regardless of where they live, are increasingly exposed to similar food environments dominated by energy-dense, processed foods. From a public health perspective, these findings emphasize the need for gender-sensitive, behavior-focused, and school-based interventions that integrate physical activity promotion with nutritional education. Instead of targeting specific geographic regions, strategies should focus on improving adolescents’ perceived behavioral control and willingness for healthy choices, such as through TPB-informed apps or peer modeling, to moderate snack consumption and foster healthy, active lifestyles in both boys and girls. These findings could support the Ministry of Health’s school nutrition guidelines and adolescent physical activity standards. 47 Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the Faculty of Medicine, Universitas Udayana, Bali, Indonesia (Approval No. 2614/UN14.2.2.VII.14/LT/2025). Written informed consent was obtained from school authorities and parents/guardians of all participants. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding No external funding was received for this study. All authors contributed on a voluntary basis; funders had no role in study design, data collection, analysis, interpretation, or writing of the manuscript. Authors' contributions IPAG: conceptualization, methodology, data curation, formal analysis, writing – original draft. NW: data collection, validation, methodology, supervision, writing – review & editing. KTA: critical revision, interpretation of public health implications, writing – review & editing. All authors read and approved the final manuscript. Acknowledgements We thank the participating schools and adolescents for their involvement. References Norris SA, Frongillo EA, Black MM, et al. Adolescence as a key developmental window for nutrition promotion and cardiometabolic disease prevention. Nat Food. 2025. 10.1038/s43016-025-00999-9 . Popkin BM. The nutrition transition: an overview of world patterns of change. Nutr Rev. 2004;62(7 Pt 2):S140–3. 10.1111/j.1753-4887.2004.tb00084.x . Popkin BM. Relationship between shifts in food system dynamics and acceleration of the global nutrition transition. Nutr Rev. 2017;75(2):73–82. 10.1093/nutrit/nuw064 . Popkin BM. 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Preference for and consumption of traditional and fast foods among adolescents in Indonesia. Food Res. 2023;7(4):211–26. 10.26656/fr.2017.7(4).156 . Jomjunyong J, et al. Unhealthy dietary patterns and their associations with sociodemographic factors as predictors among underweight and overweight adolescents in Southern Thailand. Int J Environ Res Public Health. 2023;20(17):6703. 10.3390/ijerph20176703 . Appannah G, Emadian M, Sambu R, et al. Associations of an empirical dietary pattern with cardiometabolic risk factors in Malaysian adolescents. Nutr Metab (Lond). 2020;17:26. 10.1186/s12986-020-00447-x . Colozza D, Avendano M. Urbanisation, dietary change and traditional food practices in Indonesia: a longitudinal analysis. Soc Sci Med. 2019;233:103–12. 10.1016/j.socscimed.2019.06.007 . Pearson N, Griffiths P, Biddle SJH, et al. Clustering and correlates of screen-time and eating behaviours among young adolescents. BMC Public Health. 2017;17(1):533. 10.1186/s12889-017-4450-2 . Nurhasan M, Ariesta DL, Utami MMH, et al. Dietary transitions in Indonesia: the case of urban, rural, and forested areas. Food Sec. 2024. 10.1007/s12571-024-01488-3 . Cuenca-García M, Moreno LA, Ruiz JR, et al. More physically active and leaner adolescents have higher energy intake: the HELENA study. J Pediatr. 2014;164(1):87–93. 10.1016/j.jpeds.2013.07.060 . Yao L, Rhodes RE. Parental correlates in child and adolescent physical activity: a meta-analysis. Int J Behav Nutr Phys Act. 2015;12:10. 10.1186/s12966-015-0163-y . Vilhar EČ, Golja P, Starc G. Adequacy of energy and macronutrient intake in differently active Slovenian adolescents. BMC Nutr. 2023;9:58. 10.1186/s40795-023-00708-x . Al-Hazzaa HM, Abahussain NA, Al-Sobayel HI, et al. Physical activity, sedentary behaviors and dietary habits among Saudi adolescents relative to age, gender and region. Int J Behav Nutr Phys Act. 2011;8:140. 10.1186/1479-5868-8-140 . Anyanwu OA, Naumova EN, Chomitz VR, et al. The socio-ecological context of the nutrition transition in Indonesia: a qualitative investigation of perspectives from multi-disciplinary stakeholders. Nutrients. 2023;15(1):25. 10.3390/nu15010025 . Deforche B, Van Dyck D, Verloigne M, et al. Changes in weight, physical activity, sedentary behaviour and dietary intake during the transition to higher education: a prospective study. Int J Behav Nutr Phys Act. 2015;12:16. 10.1186/s12966-015-0173-9 . Stea TH, Torstveit MK. Association of lifestyle habits and academic achievement in Norwegian adolescents. Nutrients. 2014;6(8):3653–70. 10.3390/nu6083653 . Wong JE, Parnell WR, Howe AS, et al. Diet quality is associated with greater physical activity and lower sedentary time in New Zealand adolescents. Public Health Nutr. 2021;24(4):707–16. 10.1017/S1368980020001520 . Fismen AS, Smith ORF, Torsheim T, et al. Trends in food habits and physical activity among adolescents in Nordic countries, 2001–2014. Food Nutr Res. 2020;64:3676. 10.29219/fnr.v64.3676 . Vereecken C, Todd J, Roberts C, et al. Television viewing behaviour and associations with food habits in different countries. Public Health Nutr. 2005;9(2):244–50. 10.1079/PHN2005847 . Rathi N, Riddell L, Worsley A. What influences urban Indian secondary school students’ food consumption? Public Health Nutr. 2018;21(4):689–700. 10.1017/S1368980017002917 . Monteiro CA, Cannon G, Levy RB, et al. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 2019;22(5):936–41. 10.1017/S1368980018003762 . Askari M, Heshmati J, Shahinfar H, et al. Ultra-processed food and the risk of overweight and obesity: a systematic review and meta-analysis. Int J Obes. 2020;44(10):2080–91. 10.1038/s41366-020-00650-z . Neri D, Martinez-Steele E, Monteiro CA, et al. Consumption of ultra-processed foods and dietary nutrient profiles in 85 countries. Public Health Nutr. 2022;25(6):1558–72. 10.1017/S1368980022000157 . Tadesse A, et al. Effect of nutrition behavior change communication on nutrition knowledge and dietary practices of pregnant adolescents in West Arsi, Central Ethiopia: a cluster randomized controlled trial. Front Nutr. 2025;12:1541415. 10.3389/fnut.2025.1541415 . Reuters. Indonesia will give food companies two years to meet new labelling rules. Reuters. Published August 27, 2025. Accessed October 21, 2025. https://www.reuters.com/business/healthcare-pharmaceuticals/indonesia-will-give-food-companies-two-years-meet-new-labelling-rules-2025-08-27/ Unicef. Social and behaviour change communication strategy: Improving adolescent nutrition in Indonesia. UNICEF Indonesia; 2024. Additional Declarations No competing interests reported. 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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-8835544","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588820588,"identity":"1c57216a-102d-4438-a735-818ddb5b7ddf","order_by":0,"name":"I Putu Adiartha Griadhi","email":"data:image/png;base64,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","orcid":"","institution":"Udayana University","correspondingAuthor":true,"prefix":"","firstName":"I","middleName":"Putu Adiartha","lastName":"Griadhi","suffix":""},{"id":588820589,"identity":"42e66197-b812-4a77-9a28-09e4585c9c61","order_by":1,"name":"Nila Wahyuni","email":"","orcid":"","institution":"Udayana University","correspondingAuthor":false,"prefix":"","firstName":"Nila","middleName":"","lastName":"Wahyuni","suffix":""},{"id":588820590,"identity":"07bc717c-6d5e-4652-9f04-1ae953721bd6","order_by":2,"name":"Kadek Tresna Adhi","email":"","orcid":"","institution":"Udayana University","correspondingAuthor":false,"prefix":"","firstName":"Kadek","middleName":"Tresna","lastName":"Adhi","suffix":""}],"badges":[],"createdAt":"2026-02-10 02:39:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8835544/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8835544/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102380681,"identity":"9787765a-78ba-49d6-9dcd-3088e383a17c","added_by":"auto","created_at":"2026-02-11 06:41:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43165,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow diagram. From initial recruitment to final analyzed sample, showing\u003c/p\u003e\n\u003cp\u003eexclusions for implausible energy intake.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8835544/v1/a8e2819107aa77aa3df24bbf.png"},{"id":102380682,"identity":"b8ed3985-91ea-4b9c-ae74-9c5b5c3ce783","added_by":"auto","created_at":"2026-02-11 06:41:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19399,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between physical activity (PAQ-A score) and total energy intake among adolescents (n = 447). The scatter plot shows a modest positive correlation (β = +93.3, p \u0026lt; 0.001), with higher energy intake observed among more active participants. The shaded band indicates the 95% confidence interval.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8835544/v1/aebb206f277933852e344fc2.png"},{"id":102380683,"identity":"c52508de-2a36-4eea-acd3-bdd376365e35","added_by":"auto","created_at":"2026-02-11 06:41:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":586899,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship Between Energy Intake, Area, and Gender. Interaction between area, physical activity, and gender on total energy intake among adolescents (n = 447). Error bars represent standard deviations. Males show a steeper increase in energy intake with rising activity levels compared to females, indicating a stronger intake–activity linkage among boys.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8835544/v1/f91d42466aeea09e666db80f.png"},{"id":102733459,"identity":"3e1a8f95-e36b-47cb-919e-0d75a17ccdaf","added_by":"auto","created_at":"2026-02-16 05:40:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1457282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8835544/v1/23d435a4-8d66-4711-986b-e3c2ef5dea7f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physical activity and energy intake in adolescents: Gender differences and dietary pattern clustering in an ongoing nutrition transition","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdolescence is a pivotal stage in human development characterized by rapid somatic growth and the consolidation of behavioral patterns that often carry into adulthood. Nutritionally, adolescents have high energy and nutrient requirements to support growth and maturation, while at the same time they are unusually sensitive to environmental and social influences that shape food choices and physical activity.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The global phenomenon known as the nutrition transition\u0026mdash;the population-level shift from traditional diets toward increased consumption of processed, energy-dense foods and sugars\u0026mdash;has been documented across low- and middle-income countries (LMICs) and is tightly linked to concurrent declines in physical activity and increases in sedentary behavior.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e These shifts create a double burden in many settings where undernutrition persists alongside rising overweight and diet-related non-communicable diseases (NCDs).\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eChanges in adolescent diet and activity are not only driven by macroeconomic forces but also by local food environments, school canteen offerings, advertising, and social norms, which can be understood through behavioral theories such as the Theory of Planned Behavior (TPB) and the Prototype Willingness Model (PWM).\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e TPB posits that behaviors like physical activity and healthy eating are influenced by attitudes, subjective norms (e.g., peer and family expectations), and perceived behavioral control (e.g., confidence in overcoming barriers). PWM extends this by incorporating social reactive elements, such as prototype favorability (positive views of typical active/eaters) and behavioral willingness (openness to spontaneous actions), which are particularly relevant for adolescents' impulsive decisions around diet and activity. Several recent studies in Southeast Asia demonstrate that adolescents are increasingly exposed to processed snacks and sugar-sweetened beverages, which reduces dietary quality and may narrow traditional urban\u0026ndash;rural differences in intake.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e These environmental drivers are important because they shape both what adolescents choose to eat and the contexts (e.g., school break times, neighborhood stores) in which snacking occurs. Such convergence of dietary exposures across geographical strata has been hypothesized as a key mechanism behind the homogenization of adolescent diets observed in multiple LMIC contexts.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePhysical activity (PA) is a crucial, yet sometimes under-emphasized, determinant of energy balance during adolescence. Activity level influences total energy expenditure and may also be associated with patterns of energy intake: active adolescents can, in many settings, consume more calories to meet higher energy demands, while sedentary adolescents may adopt different snack-based eating patterns.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Empirical studies find modest but consistent positive associations between PA scores and total energy intake among youth \u0026mdash; associations that are often statistically significant but explain only a small fraction of interindividual variation in intake (e.g., r\u0026thinsp;\u0026asymp;\u0026thinsp;0.15\u0026ndash;0.30).\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e This implies that while PA contributes to energy intake, behavioral, cultural and environmental factors (peer norms, body image pressures, accessibility of food) also have substantial influence.\u003c/p\u003e \u003cp\u003eWithin the school setting, interventions that combine physical activity promotion and nutritional education have been shown in several contexts to improve health behaviors among children and adolescents (systematic and scoping reviews highlight the promise and limitations of combined PA\u0026ndash;nutrition programs).\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e These findings align closely with the practical policy implications of the current study, which examines how PA, gender and area interact in shaping energy consumption among adolescents.\u003c/p\u003e \u003cp\u003eDespite these global and regional insights, there is a gap in the literature regarding the joint influence of physical activity, gender, and area of residence (urban, transitional, rural) on adolescent energy intake in Indonesia. Gender differences are particularly relevant: boys typically show higher energy intakes and higher physical activity levels, while girls may exhibit greater dietary restraint related to body image concerns\u0026mdash;factors that complicate straightforward predictions of intake based on activity alone.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Guided by TPB and PWM, we hypothesize that perceived behavioral control and subjective norms (e.g., gender-specific social expectations) moderate the PA-intake relationship, with stronger links in males due to favorable prototypes of active boys. Additionally, the \u0026ldquo;transitional\u0026rdquo; areas (peri-urban or semi-urban) often combine features of both rural and urban food environments and may therefore be critical zones for observing early stages of dietary convergence. Understanding these joint influences is necessary to design school-level and community interventions that are gender-sensitive and activity-aligned.\u003c/p\u003e \u003cp\u003eAccordingly, the present study examines the association between physical activity (measured via PAQ-A) and total energy intake among adolescents, how this association differs by gender and across urban, transitional, and rural settings, and the underlying dietary patterns (via PCA) that may explain observed differences in energy consumption. To our knowledge, no prior Indonesian study has jointly examined physical activity, gender, and area of residence in relation to energy intake and dietary patterns.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted among adolescents aged 13\u0026ndash;15 years attending four public junior high schools located in urban, transitional, and rural areas of Bali, Indonesia, from August to November 2025. A total of 692 eligible adolescents completed the questionnaires (all students in selected classes who provided consent). After plausibility screening using the EI/BMR ratio (1.1\u0026ndash;2.5 cut-off to exclude under- or over-reporters, following standard nutritional epidemiology methods), 245 were excluded due to implausible energy intake. A total of 447 plausible respondents (137 males, 310 females) were included in the final analysisSample size was calculated based on a power of 0.80 to detect moderate effect sizes (f\u0026thinsp;=\u0026thinsp;0.25) in ANOVA interactions, assuming alpha\u0026thinsp;=\u0026thinsp;0.05 and accounting for clustering within schools. This study was approved by the Ethics Committee of the Faculty of Medicine, Universitas Udayana, Bali, Indonesia (Approval No. 2614/UN14.2.2.VII.14/LT/2025). Written informed consent was obtained from the school authorities and parents or guardians of all participating students. The study procedures adhered to the ethical principles of the Declaration of Helsinki and complied with STROBE-nut guidelines for observational nutritional studies (see Additional File 1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstruments and Measures\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eEnergy Intake.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eDietary intake was assessed using a validated Food Frequency Questionnaire (FFQ) adapted for Indonesian adolescents. Nutrient values were derived from the Indonesian Food Composition Database (TKPI, Ministry of HealthKemenkes RI, 2019). A recent validation study among Indonesian adolescents found moderate to good validity for a semi-quantitative FFQ to estimate sugar intake.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e The FFQ recorded frequency and portion size of major food groups over the past month; daily energy intakes (kcal/day) were calculated. Implausible intakes (EI/BMR\u0026thinsp;\u0026lt;\u0026thinsp;1.1 or \u0026gt;\u0026thinsp;2.5) were excluded, following standard methodology for adolescent nutrition surveillance.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003ePhysical Activity.\u003c/em\u003e \u003c/p\u003e \u003cp\u003ePhysical activity was measured via the Physical Activity Questionnaire for Adolescents (PAQ-A), a self-administered 7-day recall tool validated in multiple cultural settings (e.g., Spanish adolescents: reliability \u0026amp; validity established).\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Scores range from 1 (low) to 5 (high); in this study participants were further categorized into three groups \u0026ndash; Low (\u0026le;\u0026thinsp;2.0), Moderate (\u0026gt;\u0026thinsp;2.0 \u0026ndash; \u0026le; 3.0), and High (\u0026gt;\u0026thinsp;3.0) based on tertiles.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDietary Pattern.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eDietary data were derived from a semi-quantitative food frequency questionnaire (FFQ) that assessed habitual intake of various food items during the past month. To minimize reporting bias, only plausible reporters\u0026mdash;participants whose reported energy intake was within a biologically plausible range\u0026mdash;were included for this analysis (n\u0026thinsp;=\u0026thinsp;447). A total of 19 food-frequency items were selected, covering staple foods, plant-based proteins, animal proteins, fruits, vegetables, beverages, and snack foods. Items were chosen based on common Indonesian adolescent diets from prior surveys.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Categorical responses (e.g., \u0026ldquo;every day\u0026rdquo;, \u0026ldquo;1\u0026ndash;2 times per week\u0026rdquo;, \u0026ldquo;never\u0026rdquo;) were converted to times per day, using a standard conversion scale: \u0026ge; 2 times/day\u0026thinsp;=\u0026thinsp;2.0, once/day\u0026thinsp;=\u0026thinsp;1.0, 3\u0026ndash;4 times/week\u0026thinsp;=\u0026thinsp;0.47, 1\u0026ndash;2 times/week\u0026thinsp;=\u0026thinsp;0.20, 1\u0026ndash;3 times/month\u0026thinsp;=\u0026thinsp;0.07, never\u0026thinsp;=\u0026thinsp;0.00. All variables were standardized (z-scores) prior to analysis.\u003c/p\u003e \u003cp\u003ePrincipal Component Analysis (PCA) was employed to identify underlying dietary patterns based on inter-correlations among food items. Sampling adequacy was verified using the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) test (\u0026gt;\u0026thinsp;0.6) and Bartlett\u0026rsquo;s Test of Sphericity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming suitability for PCA. Components were retained based on eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1 and the Scree Plot inflection point. To improve interpretability, a Varimax orthogonal rotation was applied, and food items with absolute loadings\u0026thinsp;\u0026ge;\u0026thinsp;0.30 were used to characterize each dietary pattern. Individual component scores were calculated for each respondent to reflect adherence to each pattern, which were later used in analyses of gender differences, physical activity levels, and total energy intake.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were reported for key variables by gender, activity and area. One-way ANOVA tested differences in energy intake across activity categories and areas. Pearson correlation examined associations between PAQ-A score and energy intake. Linear regression assessed the independent effect of physical activity score (continuous) on energy intake, controlling for gender and area. A three-way factorial ANOVA (Area \u0026times; Activity \u0026times; Gender) evaluated interaction effects. PCA results were presented with factor loadings and interpreted patterns. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Analyses were performed using SPSS 26.0 (IBM Corp., Armonk, NY).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eA total of 692 adolescents were recruited and completed the questionnaires. After excluding 245 due to implausible energy intake (EI/BMR\u0026thinsp;\u0026lt;\u0026thinsp;1.1 or \u0026gt;\u0026thinsp;2.5, indicating under- or over-reporting), 447 plausible respondents (137 males and 310 females) aged 13\u0026ndash;15 years were included in the analysis (see Figure [nomor baru] for participant flow diagram). The mean age of participants was 13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 years, with no significant age difference between males and females (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The mean physical activity score (PAQ-A) for the total sample was 2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63, indicating predominantly moderate levels of habitual activity. Males demonstrated significantly higher activity levels (2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62) compared to females (2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60; t(445)\u0026thinsp;=\u0026thinsp;3.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean daily energy intake of all participants was 1897\u0026thinsp;\u0026plusmn;\u0026thinsp;443 kcal/day, with males reporting higher intakes (2056\u0026thinsp;\u0026plusmn;\u0026thinsp;463 kcal/day) than females (1799\u0026thinsp;\u0026plusmn;\u0026thinsp;402 kcal/day; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were found across areas (urban, transitional, rural; p\u0026thinsp;=\u0026thinsp;0.73), suggesting relative homogeneity in overall caloric intake across geographic locations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eexclusions for implausible energy intake.\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\u003eDescriptive Characteristics of Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAQ-A score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy Intake (kcal/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2056\u0026thinsp;\u0026plusmn;\u0026thinsp;463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1799\u0026thinsp;\u0026plusmn;\u0026thinsp;402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1897\u0026thinsp;\u0026plusmn;\u0026thinsp;443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnergy Intake by Gender and Area\u003c/h2\u003e \u003cp\u003eAcross all areas, males consistently reported higher energy consumption than females, with mean differences ranging from 200 to 300 kcal/day. Among male students, energy intake was slightly higher in transitional areas (2086\u0026thinsp;\u0026plusmn;\u0026thinsp;511 kcal/day) compared to urban (2069\u0026thinsp;\u0026plusmn;\u0026thinsp;387 kcal/day) and rural (1988\u0026thinsp;\u0026plusmn;\u0026thinsp;486 kcal/day), although these differences were not statistically significant (F(2,134)\u0026thinsp;=\u0026thinsp;0.51, p\u0026thinsp;=\u0026thinsp;0.60) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, for female students, mean energy intake did not differ significantly across areas (F(2,302)\u0026thinsp;=\u0026thinsp;0.40, p\u0026thinsp;=\u0026thinsp;0.67), with the highest average intake observed in urban schools (1818\u0026thinsp;\u0026plusmn;\u0026thinsp;432 kcal/day). These findings indicate that gender is a stronger determinant of energy intake than area of residence, and that environmental differences between rural and urban schools do not substantially affect total caloric intake.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnergy Intake by Area and Gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea\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\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (kcal/day)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2069\u0026thinsp;\u0026plusmn;\u0026thinsp;387\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\u003eTransitional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2086\u0026thinsp;\u0026plusmn;\u0026thinsp;511\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\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1988\u0026thinsp;\u0026plusmn;\u0026thinsp;486\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\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1818\u0026thinsp;\u0026plusmn;\u0026thinsp;432\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\u003eTransitional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1778\u0026thinsp;\u0026plusmn;\u0026thinsp;373\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\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1820\u0026thinsp;\u0026plusmn;\u0026thinsp;423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEnergy Adequacy\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the majority of respondents (59.1%) fell into the low adequacy category, 24.2% reported adequate intake, and 16.7% exceeded recommendations. A clear gradient was observed across areas: urban students had the highest proportion of adequate intake (32.1%), while rural students showed the greatest proportion of insufficient intake (66.1%). Although gender differences were not pronounced in adequacy distribution, male participants tended to have slightly higher proportions of both adequate and excess energy intake compared to females, aligning with their higher mean caloric consumption. These results suggest that while mean energy intake does not differ significantly by area, a larger proportion of rural adolescents experience suboptimal caloric adequacy, potentially reflecting lower dietary diversity or meal frequency.\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\u003eEnergy Adequacy by Area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (\u0026lt;\u0026thinsp;90% RDA)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdequate (90\u0026ndash;110%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;110%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransitional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eRelationship Between Physical Activity and Energy Intake\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes the relationship between energy intake and physical activity level. A clear upward trend was observed: adolescents classified in the high activity category reported the highest mean energy intake (2051.8\u0026thinsp;\u0026plusmn;\u0026thinsp;523.8 kcal/day), followed by moderate (1868.3\u0026thinsp;\u0026plusmn;\u0026thinsp;438.6 kcal/day) and low activity groups (1839.9\u0026thinsp;\u0026plusmn;\u0026thinsp;404.1 kcal/day). ANOVA results confirmed significant differences across activity levels (F(2,444)\u0026thinsp;=\u0026thinsp;6.23, p\u0026thinsp;=\u0026thinsp;0.002). Post-hoc comparisons (Tukey HSD) revealed that the high vs. low activity groups differed significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while moderate and low groups did not. This indicates that students with higher physical activity levels tend to consume more energy, consistent with physiological expectations of energy balance.\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\u003eEnergy Intake by Physical Activity Category\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=\"\u0026plusmn;\" 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\u003ePhysical Activity Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (kcal/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1839.9\u0026thinsp;\u0026plusmn;\u0026thinsp;404.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1868.3\u0026thinsp;\u0026plusmn;\u0026thinsp;438.6\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2051.8\u0026thinsp;\u0026plusmn;\u0026thinsp;523.8\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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRegression Analysis Between PAQ-A Score and Energy Intake\u003c/h2\u003e \u003cp\u003eTo further examine the relationship between activity and energy intake, a linear regression analysis was performed using total energy intake as the dependent variable and PAQ-A score as the predictor. Results showed a significant positive association (β = +93.3, SE\u0026thinsp;=\u0026thinsp;25.7, t\u0026thinsp;=\u0026thinsp;3.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The model explained 2.9% of the variance in total energy intake (R\u0026sup2; = 0.029), indicating a modest yet statistically significant effect. In practical terms, each one-point increase in physical activity score corresponded to an average increase of 93 kcal/day in total energy consumption. Although the coefficient of determination was low, this finding supports the existence of a meaningful behavioral linkage between activity and energy intake.\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear Regression of Physical Activity and Energy Intake\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\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1695.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.7\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\u003ePAQ-A score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.64\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInteraction Effects of Area, Physical Activity and Gender\u003c/h2\u003e \u003cp\u003eThe three-way ANOVA results (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) revealed that physical activity (p\u0026thinsp;=\u0026thinsp;0.006) and gender (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly influenced energy intake, while area (p\u0026thinsp;=\u0026thinsp;0.73) had no effect. A significant interaction between activity and gender (p\u0026thinsp;=\u0026thinsp;0.034) indicated that the effect of activity on energy intake was stronger among males. No significant interactions involving area were detected, suggesting that the observed relationships between activity and intake were consistent across urban, transitional, and rural environments. Graphical inspection (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) confirmed that males exhibited steeper increases in energy intake with rising activity levels, while females showed a flatter trend.\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 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThree-Way ANOVA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\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\u003e27.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓✓\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity \u0026times; Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDietary Patterns Identified by PCA\u003c/h2\u003e \u003cp\u003eThree principal dietary patterns were identified, together explaining approximately 65% of the total variance in food frequency data. The rotated component loadings are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Pattern 1 (Plant-based Traditional): characterized by higher consumption of tempeh, tofu, and fish, reflecting a traditional Indonesian diet rich in local plant-based protein sources. Pattern 2 (Mixed): showed no dominant food items, likely representing a transitional eating behavior between traditional and modern foods. Pattern 3 (Processed/Junk-food): defined by frequent instant noodle consumption and lower rice intake, indicating a shift toward convenience-oriented, ultra-processed foods. The cumulative variance and scree plot confirmed adequate factor stability.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVarimax-rotated loadings of food items on three dietary patterns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePattern 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePattern 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePattern 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTempeh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTofu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstant noodles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePattern Scores and Associations\u003c/h2\u003e \u003cp\u003ePCA scores were computed for each respondent and compared across gender and physical activity categories. Subgroup analyses showed that adolescents with moderate-to-high physical activity had higher scores for the Plant-based Traditional pattern (p\u0026thinsp;\u0026asymp;\u0026thinsp;0.055), while the Processed/Junk-food pattern was more prevalent in low-activity groups (p\u0026thinsp;=\u0026thinsp;0.502). No significant gender differences in pattern adherence were found, but trends suggested females favored traditional patterns.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Correlation analyses (Spearman\u0026rsquo;s ρ) between dietary pattern scores and total energy intake revealed no statistically significant relationships (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all patterns), although a weak positive trend was observed for the Processed/Junk-food pattern (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of dietary pattern scores with total energy intake and physical activity\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation (ρ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANOVA F\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlant-based Traditional\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeak (ρ\u0026thinsp;\u0026lt;\u0026thinsp;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMixed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcessed/Junk-food\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeak (ρ\u0026thinsp;\u0026lt;\u0026thinsp;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn particular, the Plant-based Traditional pattern appears consistent with more active lifestyles and may represent a protective dietary habit, while the Processed/Junk-food pattern reflects modernized, low-activity consumption behavior increasingly observed in adolescents. Dietary patterns are converging across areas, with coexistence of traditional and modern snack-based diets reflecting an ongoing nutrition transition.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study contributes to the evolving understanding of adolescent energy balance by examining how physical activity, gender and area of residence relate to energy intake among adolescents \u0026mdash; and by exploring underlying dietary patterns. The findings highlight that behavior rather than geography appears to dominate energy intake, and they provide empirical support for the notion of dietary convergence in a country undergoing nutrition transition.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePhysical Activity, Gender and Energy Intake\u003c/h2\u003e \u003cp\u003eOur results show that adolescents with higher physical activity scores consumed significantly more energy than their less active peers, and that male adolescents consumed more energy than females. This pattern aligns with the physiological energy-balance principle: increased expenditure via physical activity typically necessitates greater intake. For instance, in the HELENA and EYHS studies, vigorous and moderate physical activity were positively associated with higher energy intake in adolescents.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e More recently, a systematic review found that acute physical activity in children and adolescents leads to modest increases in caloric intake, though the effect size remains small.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e In our own data the regression model explained approximately 3% of the variance in energy intake (R\u0026sup2; \u0026asymp; 0.03), which is consistent with previous literature suggesting that physical activity is only one of many determinants of energy intake.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e The stronger effect of physical activity on energy intake among males (significant Activity \u0026times; Gender interaction) suggests gender-specific pathways, potentially explained by TPB's perceived behavioral control (e.g., boys perceiving fewer barriers to active lifestyles) and PWM's prototype favorability (e.g., positive social reactions to 'active boys' in Indonesian culture, leading to greater willingness for energy-matching intake).\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e This aligns with evidence that adolescent boys tend to eat more and be more active than girls, amplifying the intake-activity link.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLack of Area Effect and Dietary Convergence\u003c/h2\u003e \u003cp\u003eA notable finding is the lack of a significant difference in energy intake across areas (urban, transitional, rural). On the surface, this might appear counterintuitive given the presumed differences in food environment and access between urban and rural schools. However, this result may indicate behavioral and dietary convergence across regions. In other words, the rural and transitional school settings in our study may already mirror the urban food and snack environments \u0026mdash; possibly via school canteens, packaged snack availability, or broader penetration of processed foods. This interpretation is supported by recent research in Indonesia. For example, Nurhasan et al. found evidence of declining diet quality and increasing consumption of processed and ultra-processed foods across urban, rural and forested areas, indicating the nutrition transition is pervasive and not confined to urban zones.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Furthermore, expert qualitative work has highlighted that modernization, retail penetration and changing food norms are driving convergence of diets in Indonesia.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e From a policy lens, this suggests that traditional geographic targeting (i.e., \u0026ldquo;rural vs urban\u0026rdquo;) may be less effective than focusing on behavioral and environmental interventions that cut across regions.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDietary Patterns and Their Relationship with Physical Activity, Gender, and Energy Intake\u003c/p\u003e \u003cp\u003eThe identification of three distinct dietary patterns\u0026mdash;Plant-based Traditional, Mixed, and Processed/Junk-food\u0026mdash;offers insight into the coexistence of traditional and modernized eating habits among adolescents. The Plant-based Traditional pattern, characterized by higher consumption of tempeh, tofu, and fish, likely reflects adherence to culturally embedded dietary habits. In contrast, the Processed/Junk-food pattern, marked by frequent instant noodle consumption and reduced rice intake, illustrates an emerging shift toward convenience-oriented, energy-dense foods.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA noteworthy finding from this study is the trend toward higher adherence to the Plant-based Traditional pattern among adolescents with moderate-to-high physical activity levels (p\u0026thinsp;\u0026asymp;\u0026thinsp;0.055). This suggests that students who are more physically active tend to maintain healthier, locally traditional diets, a relationship supported by evidence that physical activity is often positively associated with prudent or health-conscious eating behaviors.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e These results align with studies indicating that active adolescents are more likely to consume fruits, vegetables, and home-prepared meals rather than processed snacks or fast foods.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGender-related tendencies may also play a role in shaping these patterns, consistent with TPB's subjective norms (e.g., girls facing stronger norms for dietary restraint).\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Previous research has shown that female adolescents are more likely to adopt health-oriented dietary patterns, whereas males often consume more energy-dense and processed foods.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Although gender differences were not directly tested within the PCA, it is plausible that females contributed more strongly to the Plant-based Traditional pattern, consistent with behavioral evidence in similar age groups. However, this was not directly tested. In the broader context of adolescent nutrition, these behavioral distinctions are influenced not only by biological factors but also by social and environmental exposures\u0026mdash;including peer norms, body image concerns, and school food availability.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite identifying these patterns, no significant correlations were found between dietary pattern scores and total energy intake. This is consistent with previous findings among adolescents, where self-reported dietary frequency data often fail to reflect true energy intake due to underreporting or variation in portion size.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Nevertheless, the weak positive trend observed between the Processed/Junk-food pattern and energy intake may indicate compensatory behaviors, such as higher caloric density from processed foods despite lower meal frequency. This aligns with evidence linking ultra-processed food consumption to greater energy density and poorer diet quality.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePublic Health Implications\u003c/h2\u003e \u003cp\u003eFrom a public health standpoint, these findings underscore a critical behavioral duality among adolescents: the persistence of a protective, traditional dietary pattern in physically active groups, alongside the rapid emergence of processed, convenience-based dietary habits that may pose long-term metabolic risks. This duality is consistent with patterns observed in other middle-income countries undergoing the nutrition transition, where traditional diets coexist with ultra-processed food consumption.\u003csup\u003e42\u003c/sup\u003e Interventions promoting healthy lifestyles among youth should therefore adopt an integrated behavioral approach, informed by TPB and PWM, such as enhancing perceived behavioral control through skill-building and leveraging positive prototypes via peer-led campaigns. For example, eHealth interventions (e.g., apps tracking PA and diet with gender-tailored feedback) have shown promise in RCTs for improving adolescent behaviors.\u003csup\u003e43,44\u003c/sup\u003e Schools and communities could play a pivotal role by encouraging access to nutritious, culturally relevant foods\u0026mdash;such as tempeh and vegetables\u0026mdash;while restricting ultra-processed snacks and sugary drinks in the school environment.\u003c/p\u003e \u003cp\u003eGiven our findings, school-based interventions in Indonesia should emphasize integrated nutrition and physical activity promotion, with sensitivity to gender and activity level, rather than relying solely on \u0026ldquo;urban vs rural\u0026rdquo; stratification. For example: Physical education (PE) classes could include nutrition-education modules tailored to activity demands and gender (e.g., ensuring female adolescents are sufficiently active and their energy intake supports growth). The school canteen should limit availability and marketing of high-sugar and high-fat snacks, promoting staple-based lunches and healthy snacks, across all school settings. The Indonesian Government\u0026rsquo;s new labelling rules for food companies (effective by 2027) are timely and aligned with such needs.\u003csup\u003e45\u003c/sup\u003e These regulatory developments may support future school-based nutrition strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eStrengths of this study include simultaneous measurement of physical activity, plausibility-adjusted energy intake and dietary patterning across multiple residential contexts, which is rare in Indonesian adolescent nutrition research. Use of validated instruments (PAQ-A, FFQ) enhances comparability with international studies. Nonetheless, important limitations exist. The exclusion rate of 245 from 692 recruited (35%) for implausible energy reporting is common in adolescent self-report studies using FFQ but may introduce selection bias toward 'accurate reporters' (e.g., more conscientious or health-conscious individuals). This rigorous screening enhances data quality by minimizing misreporting bias, though it reduces the effective sample size and may limit generalizability to the broader adolescent population. The cross-sectional design precludes causal inference; for instance, whether higher activity leads to higher intake or vice versa cannot be determined, limiting our ability to infer temporal behavioral pathways. Self-reported FFQ and activity questionnaires may involve bias (e.g., underreporting by females due to social desirability, or overreporting of activity), potentially attenuating associations. We recommend future studies incorporate sensitivity analyses for underreporting and objective measures (e.g., accelerometry for PA). The low variance explained by physical activity suggests unmeasured determinants (meal frequency, snack purchasing behavior, socio-economic status, body image) may play major roles. Additionally, although the area classification was urban/transitional/rural, the study sample was school-based in Bali and may not fully represent national Indonesian adolescent populations (e.g., remote islands or urban Java); findings should be generalized cautiously to Bali contexts, with replication needed for broader applicability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFuture Directions\u003c/h2\u003e \u003cp\u003eFuture research should adopt longitudinal designs to track how adolescent energy intake, physical activity and dietary patterns evolve over time, especially as food systems continue to change, allowing for causal inference on behavioral mechanisms. Qualitative research exploring the motivations behind snacking, gendered food behaviors and peer influences would complement quantitative findings. Furthermore, intervention trials integrating physical activity promotion, nutrition education and school-canteen reforms, with outcomes on energy intake and growth/adiposity \u0026mdash; are urgently needed in the context.\u003csup\u003e46\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides new evidence that among adolescents in Bali, physical activity and gender are the primary determinants of energy intake, whereas geographical area (urban, transitional, rural) plays only a minor role. Male adolescents and those with higher levels of physical activity consumed significantly more energy, reflecting physiological energy balance and behavioral differences between genders.\u003c/p\u003e \u003cp\u003eThe absence of significant area effects, combined with the identification of both traditional and modern/snacking dietary patterns across all settings, suggests a behavioral and dietary convergence consistent with an ongoing nutrition transition. This convergence implies that adolescents, regardless of where they live, are increasingly exposed to similar food environments dominated by energy-dense, processed foods.\u003c/p\u003e \u003cp\u003eFrom a public health perspective, these findings emphasize the need for gender-sensitive, behavior-focused, and school-based interventions that integrate physical activity promotion with nutritional education. Instead of targeting specific geographic regions, strategies should focus on improving adolescents\u0026rsquo; perceived behavioral control and willingness for healthy choices, such as through TPB-informed apps or peer modeling, to moderate snack consumption and foster healthy, active lifestyles in both boys and girls. These findings could support the Ministry of Health\u0026rsquo;s school nutrition guidelines and adolescent physical activity standards.\u003csup\u003e47\u003c/sup\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Faculty of Medicine, Universitas Udayana, Bali, Indonesia (Approval No. 2614/UN14.2.2.VII.14/LT/2025). Written informed consent was obtained from school authorities and parents/guardians of all participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this study. All authors contributed on a voluntary basis; funders had no role in study design, data collection, analysis, interpretation, or writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIPAG: conceptualization, methodology, data curation, formal analysis, writing – original draft. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNW: data collection, validation, methodology, supervision, writing – review \u0026amp; editing. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKTA: critical revision, interpretation of public health implications, writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participating schools and adolescents for their involvement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNorris SA, Frongillo EA, Black MM, et al. 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UNICEF Indonesia; 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"physical activity, adolescents, energy intake, gender differences, dietary patterns, nutrition transition, clustering","lastPublishedDoi":"10.21203/rs.3.rs-8835544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8835544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdolescence is a critical period for developing healthy lifestyle behaviors. Understanding the relationship between physical activity and energy intake is essential for promoting health and preventing early onset of metabolic risk.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study examines gender differences in the associations between physical activity and energy intake among adolescents, alongside dietary pattern clustering during an ongoing nutrition transition.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted among 447 plausible respondents aged 13\u0026ndash;15 years. Energy intake was assessed using a validated FFQ, physical activity using the PAQ-A, and dietary patterns identified through principal component analysis (PCA). Statistical analyses included ANOVA, correlation, and regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEnergy intake differed significantly by physical activity level (p\u0026thinsp;=\u0026thinsp;0.002) and gender (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not by area (p\u0026thinsp;=\u0026thinsp;0.73). PCA identified three dietary patterns explaining 65% of variance: plant-based traditional, mixed, and processed/junk-food. Their coexistence reflects an ongoing nutrition transition among adolescents. The interaction between gender and physical activity was significant (p\u0026thinsp;=\u0026thinsp;0.034), indicating stronger intake-activity linkage among males.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEnergy consumption among adolescents is primarily influenced by physical activity and gender, reflecting behavioral rather than geographic determinants. The coexistence of traditional and modern dietary patterns indicates an ongoing nutrition transition requiring gender-responsive and school-based health education strategies integrated into national adolescent nutrition programs.\u003c/p\u003e","manuscriptTitle":"Physical activity and energy intake in adolescents: Gender differences and dietary pattern clustering in an ongoing nutrition transition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 06:41:04","doi":"10.21203/rs.3.rs-8835544/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c0a8709-957e-413d-8702-311e9da7d35c","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-20T02:07:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 06:41:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8835544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8835544","identity":"rs-8835544","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}