Cutoff values of the Physical Activity Questionnaire in Adolescents (PAQ-A) and their relationships with morphophysiological variables in rural and urban populations

Cutoff values of the Physical Activity Questionnaire in Adolescents (PAQ-A) and their relationships with morphophysiological variables in rural and urban populations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cutoff values of the Physical Activity Questionnaire in Adolescents (PAQ-A) and their relationships with morphophysiological variables in rural and urban populations Yina Marcela Diaz Buitrago, Ever Daniel Buitrago Vargas, Armando Monterrosa-Quintero This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6284518/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Assessing physical activity levels and their associations with morphophysiological variables is crucial for promoting adolescent health and guiding interventions. This study aimed to: (a) categorize physical activity levels using percentiles from the Physical Activity Questionnaire for Adolescents (PAQ-A), (b) compare morphophysiological variables (grip strength, reaction time, foot morphology) across genders and rural/urban settings, and (c) analyze correlations between physical activity and these variables in adolescents. Methods A descriptive, correlational, and comparative study with a mixed-methods approach was conducted with 80 adolescents (15–17 years) from Huila, Colombia. Physical activity was measured with the PAQ-A, and variables including right-hand grip strength, reaction time, plantar footprint, and anthropometric measurements (height, weight, BMI) were assessed. The data were analyzed using MANOVA, ANOVA, Pearson’s correlations, and chi-square tests. Results Physical activity levels were categorized into percentiles: 25th = 1.89, 50th = 2.46, 75th = 2.90. Significant regional differences were found in plantar footprint (p ≤ 0.05), with higher values in rural adolescents, and gender differences in height, grip strength, and reaction time (p ≤ 0.05), with males outperforming females. Small to moderate correlations were observed between PAQ-A scores and grip strength (r = 0.24, p ≤ 0.05) and reaction time (r = -0.37, p ≤ 0.01). Conclusions The PAQ-A (Cronbach’s α = 0.86) is reliable for categorizing adolescent physical activity. Higher activity levels may be linked to greater grip strength and faster reaction times, with gender and regional differences in foot morphology and physical performance. However, memory and social desirability biases in the PAQ-A may affect the accuracy of associations. Tailored interventions and objective measurements in future studies are needed to optimize physical fitness and physical outcomes in adolescents. Exercise Body composition Performance Adolescents Physical activity Figures Figure 1 Introduction The Physical Activity Questionnaire for Adolescents (PAQ-A) Physical activity is defined as any movement generated by muscles that involves energy expenditure, and plays a fundamental role in preventing non-communicable chronic diseases [ 1 ]. The literature highlights a wide range of benefits of physical activity across all age groups, particularly emphasizing cardiovascular health, bone density, and mental well-being [ 2 ]. In younger populations, particularly children and adolescents, physical activity supports optimal physical development, cognitive function, and social skills [ 3 ], which translates into adults with higher cognitive levels, better physical functionality, and improved psychological health [ 4 ]. The Physical Activity Questionnaire for Adolescents (PAQ-A) has become a widely used tool for assessing physical activity levels in youth aged 14 to 20 years old. Its application in international research has been supported by a strong internal consistency and an acceptable validity across a variety of contexts [ 5 , 6 ]. Cutoff points above 2.73 or 2.75 have been proposed to identify adolescents who meet international physical activity recommendations [ 7 ]. The PAQ-A has also shown moderate correlations with more objective assessment methods such as accelerometry [ 6 , 8 ], reinforcing its usefulness in settings where access to advanced technology may be limited. Translated versions, including the Spanish and Arabic adaptations, have demonstrated reliable and valid psychometric properties, supporting their use in cross-cultural studies [ 9 , 10 ]. Notably, the use of instruments such as the PAQ-A has revealed that urban adolescents exhibit higher levels of inactivity, with a non-compliance value of 33.2% on the basis of MET values [ 11 ]. Therefore, questionnaires such as the PAQ-A, alongside other complementary variables, serve as essential tools for assessing multiple aspects of research, including physical activity, dietary quality, and level of education. Despite its strengths, the application of the PAQ-A presents several challenges due to its reliance on self-reported data. Adolescents may overestimate or underestimate their physical activity levels due to factors such as memory bias, misinterpretation of questions, or social desirability bias, which can affect the accuracy of the reported data [ 12 , 13 ]. Additionally, the PAQ-A does not capture specific details about the intensity, duration, or frequency of physical activities, limiting its ability to provide a comprehensive picture of activity patterns compared to objective methods like accelerometry [ 14 ]. The influence of external factors, such as cultural or environmental contexts, may further complicate the interpretation of PAQ-A results, as these can shape adolescents’ perceptions and reporting of physical activity [ 15 ]. These challenges highlight the importance of complementing the PAQ-A with objective measures in settings where resources permit, to enhance the validity and reliability of physical activity assessments in adolescent populations. Morphophysiological variables in adolescent populations Morphophysiological variables play a significant role in human research, influencing various aspects of health and performance. Anthropometric measurements and somatotypes are essential for categorizing and identifying functional strengths or weaknesses [ 16 ]. In this context, standardizing controllable factors such as diet, physical activity, and medication, is crucial for obtaining reliable results in physiological studies [ 17 ]. Importantly, biosocial factors significantly impact morphophysiological adaptations in urban environments [ 18 ]. The proper measurement of biological variables, referred to as "physiometry", is fundamental for ensuring replicable and efficient physiological research [ 19 ]. When measurements are reliable, morphometric data become essential for athlete selection and sports specialization [ 16 ]. A noteworthy example is the regulation of body temperature under thermal stress, which can vary depending on morphological characteristics, intrinsic factors, diseases, and injuries [ 20 ]. The literature emphasizes the importance of adequate measurement properties of biological variables to ensure replicable and efficient research [ 19 ]. In this context, upper limb speed and strength have been identified as key physical attributes associated with improved performance in young athletes [ 21 ]. Considering the influence of physical activity on morphophysiological variables (defined as the science dedicated to understanding organ structure and functioning, which also studies macro and micro aspects of the human body such as footprint, response time, and body components) [ 22 ] during adolescence, there are still gaps in understanding how these variables relate to rural and urban populations, as well as gender-based differences. In this context, it becomes essential to classify physical activity levels in adolescents in a practical and straightforward manner using percentiles. This approach allows for a more accurate assessment of the study population by identifying specific correlations between physical activity and the various variables presented here (morphophysiological and sociodemographic). Therefore, the present study aims, first, to categorize physical activity levels in adolescents; second, to examine the relationships between these levels and the study variables; and third and finally, to compare those variables across groups and between genders to support the development of targeted intervention strategies. Materials and methods Study Type This study follows a descriptive, correlational, and comparative design with a mixed-methods approach. The research was conducted with adolescents between August, 2023 and April, 2024. By employing body assessment techniques, physical performance evaluations, and data collection through surveys, interviews, and other tools, quantitative and qualitative methods were used to analyze physical activity levels and morphophysiological variables. The sample size was determined based on the total number of students in the school grades where the study was conducted, using a sample size calculator for finite populations. Participants The study population consisted of students from three secondary education schools, specifically from the 10th and 11th grades, with a total of four groups, yielding a sample of eighty participants from both rural and urban areas of Algeciras, Huila, Colombia. The sample was evenly distributed, with 50% coming from rural areas and 50% coming from urban areas. In terms of gender distribution, the female sample included twenty rural and 21 urban participants, whereas the male sample included 20 rural and 19 urban participants. The sample was selected based on convenience, considering logistical factors such as the geographic proximity of the educational institutions and the public safety conditions in the study area. Convenience sampling facilitated student participation and ensured safety during the data collection process. Inclusion criteria To be eligible for the study, participants had to meet the following criteria: A parent or guardian had to attend an informational meeting and sign the informed consent document. Students had to be legally enrolled in the grades where the study was conducted, as verified by their student ID. The participants had to be in good health and free of viral infections or recent injuries. Both the students and their guardians agreed to participate in the study by signing the authorization document. Exclusion Criteria Participants with recent musculoskeletal injuries or chronic orthopedic conditions affecting movement or physical performance. Presence of acute or chronic illnesses that may influence physical activity or physiological measurements (e.g., cardiovascular diseases, respiratory infections). Students currently under medication that affects neuromuscular function or physical capabilities. Individuals with cognitive or developmental disorders that could interfere with understanding or performing the assessment protocols. Lack of informed consent from the participant or their legal guardian. Absence from school or inability to attend the testing sessions on scheduled dates. Participants engaged in competitive sports training that could bias physical fitness outcomes. The sociodemographic, morphofunctional, and physical activity characteristics of the participants, classified by group and sex, are presented in Tables 1 and 2 . Procedures To collect the data, an official letter was sent to the academic coordinators of the participating schools, explaining the project's nature, scope, benefits, potential discomforts, and risks. After the letter was delivered, a meeting was arranged with the school principal, who granted permission to conduct the study. Subsequently, meetings were held with parents to explain the study details and distribute the informed consent form for their signature. Next, the physical education teacher responsible for the 10th and 11th grades was contacted and agreed to allocate class time for the study's testing protocol. Evaluations were conducted in classrooms to ensure optimal conditions for participant comfort and performance. All procedures took place in the morning, following the same protocol and using identical measurement equipment at each test site. Parental or guardian authorization was confirmed through a checklist detailing the procedures each student would undergo. The study consisted of two phases. The first phase involved administering the Physical Activity Questionnaire for Adolescents (PAQ-A), following the guidelines of Kowalski et al (2004) [ 23 ]. After completing the questionnaire, various morphophysiological tests were conducted. Researchers underwent prior training and conducted two pilot tests before data collection. Variables, Protocols, and Measurement Equipment A factorial design was employed in the study, encompassing six research variables: Population Factor, divided into two levels (rural and urban). Sex Factor, divided into two levels (male and female). Asymmetry Index Factor, divided into two levels (left and right). Physical Activity Factor, subdivided into three levels (low, moderate, and high). Handgrip Strength Factor, subdivided into three levels (weak, normal, and strong). Footprint Factor, subdivided into six levels (normal high arch, normal flat, strong high arch, normal, high arch, and flat). Basic Measurements (Height, Weight, and BMI) For the anthropometric measurements, the participants were required to arrive at 7:00 a.m. wearing sports clothes. Body weight was measured using an Omron HBF-514C digital scale, with a maximum capacity of 150 kg and an accuracy of 0.1 kg. Participants stood upright, centered on the scale, with feet evenly aligned to distribute their weight uniformly. Height was measured with a SECA 206 wall-mounted stadiometer (Hamburg, Germany), with an accuracy of 1 mm. The participants stood upright with their feet together and their hands relaxed at their sides. The evaluator applied cephalic traction to align the head according to the Frankfort horizontal plane, using their thumbs behind the ears. An assistant recorded the height by lowering the stadiometer cursor [ 24 ]. Each measurement was performed twice. If the degree of discrepancy exceeded 1%, a third measurement was taken, and the final value was the average of the three measurements. The body mass index (BMI) was calculated via the Quetelet equation. Physical Activity Questionnaire for Adolescents (PAQ-A) This questionnaire assesses weekly physical activity levels in adolescents from 9th to 12th grade, aged 14–20 years old, via a Likert scale. It consists of nine items evaluating various aspects of physical activity. The questionnaire was administered electronically (via mobile phones and tablets) in urban areas, whereas a printed version was used in rural areas. The participants were informed that the questionnaire was not a performance test but rather a tool to record their actual physical activity over the past seven days. Data collection, tabulation, and analysis followed the protocol outlined by Kowalski et al (2004) [ 23 ]. The internal consistency of the Physical Activity Questionnaire for Adolescents (PAQ-A) was evaluated in the total sample, which included participants from both urban and rural areas. The Cronbach’s alpha test yielded a value of 0.86, indicating the high reliability of the instrument. This result suggests that the responses obtained from the PAQ-A exhibit a strong internal coherence, allowing the measurement to be considered consistent and reproducible within the analyzed population. The high reliability of the questionnaire supports its suitability for assessing physical activity levels in adolescents, regardless of their geographical context. Handgrip Strength To assess isometric hand strength, the participants performed three attempts with each hand, with a two-minute rest interval between measurements. The participants remained seated, with the tested arm positioned at a 90° elbow angle. The result was the average of the three recorded values. The measurements were taken with a Trailite® TL-LSC 100 digital dynamometer (Germany), with a tolerance of ± 0.5 kg (1 lb). The device was adjusted to fit each participant’s hand size. Both hands were tested to identify potential asymmetries in grip strength [ 25 ]. The strength classification followed the reference table from the dynamometer manual, defining weak grip strength as 44.3 kg for individuals aged 14–15 (LiteXpress GmbH; © Copyright TrailLite 2018). Hamstring Length (Sit-and-Reach Test) The Acuflex 1 tester (a modified sit-and-reach box from Novel Products, Inc., USA) was used for this assessment. The participants were instructed to sit with their legs fully extended, ankles in a neutral dorsiflex position, and heels against the device. They placed one hand over the other in alignment and extended their arms while bending the trunk forward in a controlled manner along the measurement surface. Each participant performed three attempts, and the final recorded value was the average of the three, expressed in centimeters [ 26 ]. Simple reaction time Reaction time was assessed using a lightweight, rigid 80 cm rod marked with a centimeter scale and weighted with a rubber disk at one end. The participants sat with their dominant arm extended, resting on a table, with an open hand positioned around the disk without touching it. The alignment was standardized to ensure that the disk's upper edge was level with the participant's hand. The examiner held the device vertically and released it three times, with random intervals of 2–5 seconds between trials [ 27 ]. The reaction time was calculated via the following equation: t \(\:=\frac{\sqrt{2\text{*}\text{h}}}{\text{g}}\) (1) where t represents the reaction time, h represents the drop distance in centimeters, and g represents gravitational acceleration (980 cm/s²). Footprint measurement Several studies have identified an association between foot morphology, assessed through plantar footprint analysis, and physical activity levels [ 28 ]. The plantar footprint reflects biomechanical characteristics that may influence posture and stability, thereby affecting physical fitness. In children and adolescents, the presence of flat feet or alterations in the plantar footprint may be linked to postural deviations that limit mobility and physical performance [ 29 ]. Therefore, evaluating the plantar footprint is essential to identify potential factors that impact physical fitness, and to guide preventive and functional improvement interventions. The quantification of the plantar footprint was conducted via the Hernández Corvo Index (HCI), a protocol that evaluates the arch of the foot by referencing the height of the forefoot in relation to the heel (As shown Fig. 1 ). Key Tracings in the Hernández Corvo Index This method has been widely used in various studies [ 29 – 31 ]. The participants stood in an orthostatic position on 210 mm thermal paper (fax), with their feet previously moistened with alcohol-based antibacterial gel, maintaining this posture for two minutes to record the plantar impressions [ 1 ]. After the established time, the footprints were outlined. With these lines, the width of the metatarsus (X) and the width of the external plantar arch (Y) were determined, and these measurements were then used to apply the following formula: HCI \(\:=\frac{X-Y}{\text{X}}.100\) (2) The result of this formula allows us to classify the foot as flat: < 39%, normal: (40% to 60%) [ 32 , 33 ]. Statistical analysis The characteristics of the participants are presented as the means and standard deviations (SD) through descriptive analysis. Data normality was assessed via the Kolmogorov‒Smirnov test, whereas variance homogeneity was verified via Levene's test. To meet the assumptions of parametric statistical analyses, data exhibiting non-normal distributions were logarithmically transformed and then re-analyzed to confirm normality before proceeding with parametric tests. To analyze the differences between groups based on the study factors, a multivariate analysis of variance (MANOVA) was performed, considering Pillai’s trace as the contrast statistic. Differences by sex were determined via a one-way analysis of variance (ANOVA), complemented by Tukey's post hoc test. Correlations between variables were analyzed via Pearson’s test, and associations were evaluated via the chi-square test. All analyses were conducted with a significant level of p ≤ 0.005. Results Categorization of Physical Activity Levels in Adolescents Since no standardized criteria were found in the literature for classifying physical activity levels using percentiles from the Physical Activity Questionnaire for Adolescents (PAQ-A), distinct categories were established based on the distribution of scores obtained in the sample studied. Percentiles were calculated based on the overall distribution of PAQ-A scores for the entire sample, considering a limited age range (15–17 years old); therefore, ages were grouped. The analysis was conducted collectively, as no statistically significant differences were found between regional groups or by gender. Percentiles were determined using the standard empirical method through the percentile function available in the Jamovi software. The 75th percentile was used as a cut-off point to define high levels of physical activity, in line with previously reported criteria in the literature [ 34 ]. The descriptive percentile values for the total sample were: 25th percentile = 1.89, 50th percentile (median) = 2.46, and 75th percentile = 2.90. This classification allows for a precise interpretation of physical activity levels in adolescents within the studied context. This classification allows for a more precise interpretation of the data within the specific population context, facilitating future comparisons and applications in similar studies. Although this study found a high internal consistency for the PAQ-A (Cronbach’s α = 0.86), as well as no significant differences by sex or group, these preliminary results suggest that the instrument is a reliable tool for assessing physical activity in adolescents from both urban and rural settings. However, to strengthen this conclusion, further reliability (test-retest) and validity analyses are recommended, including comparisons with objective measurement methods and the evaluation of metric equivalence across subgroups. It is important to interpret these findings with caution, as they are based on a specific sample and context, which may limit their generalizability to other populations or settings. Relationships Between Physical Activity Levels and Study Variables Significant positive and negative correlations were found, ranging from small (0.1 to 0.29) to moderate (0.3 to 0.5) in magnitude, when physical activity levels were related to the study's morphophysiological variables, according to the criteria established by Hopkins (2002) [ 35 ]. Specifically, rigth-hand grip strength showed a moderate positive correlation (r = 0.24, p < 0.05). Additionally, simple reaction time was negatively correlated with physical activity levels (r = -0.37, p < 0.01), indicating that higher physical activity is associated with a higher right-hand strength and faster reaction times. The chi-square test results indicated a significant association between sex and handgrip strength in both the right hand (χ² = 14.57; p = 0.001) and the left hand (χ² = 10.52; p = 0.005). Additionally, the relationships between types of residence (rural or urban) and plantar footprint classification (normal high arch, normal flat, strong high arch, normal, high arch, flat) were analyzed. A significant association was found for the left foot plantar footprint with residence type (χ² = 11.42; p = 0.044), suggesting that the distribution of footprint types varies depending on the type of residence. Comparison Between Groups Significant differences between groups were found in the plantar footprint variables for both the left and right feet (Table 1 ) via a one-way ANOVA. In both cases, the effect size was small ( ES ≤ 0.20), according to Cohen´s, criteria (1977) [ 36 ]. Table 1 . Physical activity and morphophysiological characteristics of the groups. Table 1 HERE Rural (n = 40) Urban (n = 40) PAQ-A (METs) 2.33 ± 0.58 2.46 ± 0.67 Age (years) 15.85 ± 0.80 15.95 ± 0.96 Height (cm) 163.55 ± 7.68 162.85 ± 6.95 Weight (kg) 57.21 ± 7.01 60.14 ± 9.25 BMI (kg/m²) 21.40 ± 2.42 22.69 ± 3.32 Right handgrip (kg) 31.44 ± 8.41 32.20 ± 7.30 Left handgrip (kg) 29.26 ± 7.52 30.23 ± 8.12 AI handgrip (%) 0.10 ± 0.06 0.10 ± 0.07 Trunk flexibility (cm) 27.18 ± 8.21 24.40 ± 8.57 Simple reaction time (ms) 0.23 ± 0.03 0.22 ± 0.03 Right footprint (%) 51.84 ± 10.1* 46.76 ± 11.4* Left foot footprint (%) 53.71 ± 12.13* 48.49 ± 8.88* AI footprint (%) 0.13 ± 0.15 0.09 ± 0.09 AI = asymmetry index; BMI = body mass index; PAQ-A = Physical Activity Questionnaire for Adolescents; * = differences between the groups (rural and urban). Differences in Morphophysiological and Sociodemographic Variables by Group and Gender The results reveal notable differences in morphophysiological characteristics when disaggregated by sex and group (Table 2 ). Regarding physical activity levels (PAQ-A), rural males reported the highest average (2.57 ± 0.60 METs), followed closely by urban males (2.47 ± 0.59 METs). Among females, urban adolescents were more active (2.46 ± 0.74 METs) than their rural counterparts (2.09 ± 0.46 METs). Age was consistent across all subgroups, ranging from 15.81 to 16.11 years. In terms of height, males were significantly taller than females in both settings, with rural and urban males averaging around 167.6 cm, while females averaged between 158.33 cm (urban) and 159.50 cm (rural). Urban males had the highest body weight (63.82 ± 9.54 kg), whereas BMI values were similar between urban males and females (~ 22.7 kg/m²), with rural males presenting a slightly lower BMI (20.70 ± 1.71 kg/m²). Significant gender-based differences were observed in handgrip strength. Males showed higher values in both the right and left hands. Rural males recorded the highest grip strength (right: 37.77 ± 6.77 kg; left: 35.10 ± 5.67 kg), while urban females showed slightly greater values than rural females. As for trunk flexibility, rural males exhibited the highest values (28.85 ± 9.08 cm), while urban males had the lowest (22.16 ± 7.26 cm). Reaction time was faster among males, especially in rural areas, where boys (0.21 ± 0.03 s) significantly outperformed girls (0.24 ± 0.03 s). Regarding plantar pressure distribution, rural females displayed higher right (53.33 ± 9.62%) and left (56.72 ± 8.94%) foot footprint percentages. Urban males showed the lowest values, particularly for the right foot (42.39 ± 12.79%). Differences were also noted in asymmetrical indices for plantar support and handgrip strength, although without significant patterns across all groups. In summary, while gender differences were more pronounced, particularly in strength and height, certain group-based variations emerged, especially in physical activity levels, foot morphology, and reaction time. The ANOVA results revealed significant differences in height between males and females but not within the same sex. In both cases, the effect size was large ( ES ≥ 1.2). A similar trend was observed in handgrip strength for both the right and left hands, where the difference between males and females showed a very large effect size ( ES ≥ 2.0). With respect to reaction time, significant differences were found only between rural males and females, with a large effect size ( ES ≥ 0.80). For the plantar footprint variables, differences were observed only between rural females and urban males, with a large effect size ( ES ≥ 0.80). Finally, the asymmetry index of the plantar footprint differed between urban females and rural males, with effect size values exceeding 0.80. The effect size classification followed the criteria proposed by Sawilowsky (2009) [ 37 ]. Table 2 . Morphophysiological characteristics by sex and group. Table 2 HERE Female (n = 41) Male (n = 39) Rural Urban Rural Urban PAQ-A (METs) 2.09 ± 0.46 2.46 ± 0.74 2.57 ± 0.60 2.47 ± 0.59 Age (years) 15.85 ± 0.88 15.81 ± 0.87 15.85 ± 0.75 16.11 ± 1.05 Height (cm) 159.50 ± 6.91* 158.33 ± 3.53* 167.60 ± 6.23* 167.84 ± 6.37* Weight (kg) 56.19 ± 7.51 56.81 ± 7.78 58.23 ± 6.50 63.82 ± 9.54 BMI (kg/m²) 22.10 ± 2.85 22.69 ± 3.23 20.70 ± 1.71 22.70 ± 3.51 Right handgrip (kg) 25.11 ± 3.88* 27.76 ± 4.44* 37.77 ± 6.77* 37.11 ± 6.71* Left handgrip (kg) 23.43 ± 3.49* 24.40 ± 3.97* 35.10 ± 5.67* 36.67 ± 6.49* AI handgrip (%) 0.10 ± 0.07 0.12 ± 0.08 0.10 ± 0.06 0.09 ± 0.05 Trunk flexibility (cm) 25.50 ± 7.06 26.43 ± 9.31 28.85 ± 9.08 22.16 ± 7.26 Simple reaction time (ms) 0.24 ± 0.03 & 0.22 ± 0.03 0.21 ± 0.03 & 0.22 ± 0.03 Right foot footprint (%) 53.33 ± 9.62 % 50.71 ± 8.47 50.34 ± 10.60 42.39 ± 12.79 % Left foot footprint (%) 56.72 ± 8.94 # 50.31 ± 6.64 50.70 ± 14.25 46.47 ± 10.66 # AI footprint (%) 0.10 ± 0.10 0.06 ± 0.06 f 0.17 ± 0.18 f 0.13 ± 0.11 AI = Asymmetry Index; BMI = Body Mass Index; PAQ-A = Physical Activity Questionnaire for Adolescents. * = Differences between genders (female vs. male) and not between groups (rural and urban); & = Differences between women and men in the rural region; % = Differences between rural women and rural men; # = Differences between rural women and urban men; f = Differences between urban women and urban men. Discussion This study proposed novel cut-off points for the Physical Activity Questionnaire for Adolescents (PAQ-A) and examined their relationship with morphophysiological variables in rural and urban populations. Significant differences were found between the two groups in physical activity levels, body composition, and plantar footprint patterns, suggesting that environmental context may influence both lifestyle habits and biomechanical development. Positive correlations between handgrip strength and activity levels, along with inverse associations with simple reaction time, indicate that a greater physical activity is linked to better muscular strength, neuromuscular function, and reduced asymmetries. These findings support the PAQ-A, particularly with the newly proposed cut-off values, as a practical tool for identifying physical activity levels in diverse settings. They also highlight the importance of considering morphophysiological factors when designing school and sports health interventions aimed at improving adolescent well-being and preventing injury. We have structured the discussion into different sections to facilitate the study’s comprehension, contrasting our results with the literature, proposing new lines of research, and carefully explaining our findings. Categorization of Physical Activity in Adolescents Given the scientific gap in categorizing physical activity levels in adolescents from both rural and urban populations, we classified the sample, accordingly, by stratifying the levels of physical activity into low (≤ P25), moderate (P26–P74), and high (≥ P75). While no previous studies have applied this methodology specifically to the PAQ-A using percentiles, others have used devices such as accelerometers and VO₂ max to establish reliability and cutoff points for physical activity levels in adolescents, classifying them as moderate or vigorous [ 5 , 38 ]. Another study determined a cutoff point at a value of 2.75, where values equal to or above this threshold were classified as active and lower values as inactive [ 7 ]. In our search, we identified a study with similar characteristics to ours that used percentiles to classify low, moderate, and high activity levels based on the weighted scale of the nine items comprising the instrument. The classification was as follows: low (≤ 2), moderate (> 2 and ≤ 3), and high (> 3), with the key difference being that this study was conducted on children (IPAQ-C) rather than adolescents [ 39 ]. Based on the findings of this study, and using the novel percentile-based classification, slightly higher physical activity values were observed among adolescents from urban areas (2.46 ± 0.67) as compared to their rural peers (2.33 ± 0.58), although all values fell within the same moderate physical activity range defined by the 74th percentile (> 1.89 to < 2.90). Similarly, urban adolescent girls recorded higher average scores (2.46 ± 0.74) than those from rural areas (2.09 ± 0.46). However, these differences were not statistically significant, as indicated by the MANOVA (Tables 1 and 2 ), and should therefore be interpreted with caution. These variations may reflect trends associated with contextual factors such as access to recreational spaces, sociocultural differences, or domestic workload, as reported in previous studies [ 40 , 41 ]. In contrast, rural boys displayed physical activity levels (2.57 ± 0.60) that were similar to those of their urban counterparts (2.47 ± 0.59), with both groups being within the moderate activity range. The smaller difference between rural and urban boys could be attributed to the nature of their daily activities, where rural adolescents may compensate for the lower availability of sports facilities with physical activities related to their environment, such as agricultural work or active transportation [ 42 ]. From an epidemiological perspective, the reported values suggest that a considerable percentage of adolescents in both contexts are within the moderate physical activity range (P26-P74), whereas a smaller proportion reach higher levels (≥ P75). This aligns with research that indicates a global trend of insufficient physical activity among adolescents, particularly in regions with inequalities in infrastructure and exercise promotion programs [ 42 ]. Significant Relationships Between Physical Activity and Study Variables This section presents the significant relationships identified between physical activity levels and key variables in our study, specifically handgrip strength and reaction time. These findings contribute to a better understanding of how physical activity may be associated with neuromuscular performance indicators in adolescents. The analysis aims to highlight potential patterns that could inform targeted interventions to enhance both physical fitness and motor function in this population. Handgrip Strength Findings in the literature regarding differences in handgrip strength between urban and rural adolescents are mixed. Some studies have shown that rural youth have a greater handgrip strength, with values of 32.3 vs. 25.6 kg in Spain [ 43 ]; 27.5 ± 0.3 vs. 25.6 ± 0.2 kg in Macedonia [ 44 ]; and a study in Kosovo with similar results to ours, where differences were found by gender but not by setting [ 45 ]. Moreover, other studies reported no significant differences [ 46 ]. Gender differences were consistent across all studies, with boys demonstrating a greater handgrip strength than girls [ 47 ]. Factors influencing handgrip strength include age, muscle mass, body composition, and lifestyle [ 47 , 48 ]. These results highlight the complexity of environmental, demographic, and physiological factors in evaluating handgrip strength among adolescents from different regions. Simple reaction time Several studies have indicated that age significantly influences reaction time, with a trend toward faster times in older adolescents [ 49 , 50 ]. Additionally, significant sex differences have been reported, with generally faster reaction times in males than in females, a finding that is consistent with the results of this study [ 51 , 52 ]. The relevance of this variable lies in its positive correlation with attention capacity and hand–eye reaction time in both sexes [ 51 ]. Moreover, laterality has been shown to influence reaction time, with faster responses in the dominant hand [ 53 ]. In the academic field, better school performance has been associated with optimized reaction times, suggesting a link between cognitive processes and response speed [ 52 ]. Differences in Variables According to Group and Gender Footprint analysis Despite significant differences in foot type among participants based on their region, the values obtained were within the normal ranges (40 to 54.9) as compared to the Plantar Footprint Index (PFI) (see Tables 1 and 2 ). Previous studies have reported similar values in populations with comparable characteristics, although different footprint analysis equations, such as Clarke’s, have been used. The evidence suggests that during childhood, there is a greater prevalence of flat feet (54.7% in boys and 42.7% in girls), whereas in adolescence, there is a trend towards a normal plantar structure (29.8% in boys and 35.7% in girls) across different regions [ 54 ]. Other studies have indicated that rural children tend to have higher arches, narrower midfoot areas, and lower forefoot pressures than urban children. When adjusting for weight, rural children presented approximately 22% lower forefoot pressures and 5% lower midfoot pressures [ 55 ]. An explanation for these differences between urban and rural populations could be related to footwear type, frequency of shoe usage, lifestyle, and nutritional habits. In remote regions, greater challenges exist in meeting footwear needs, conducting body assessments, and ensuring mobility due to longer distances [ 56 , 57 ]. Limitations The use of the Physical Activity Questionnaire for Adolescents (PAQ-A) as the primary tool to assess physical activity levels presents several limitations. Although the PAQ-A demonstrated high internal consistency in this study (Cronbach’s α = 0.86), self-reported instruments like this questionnaire have limited reliability and validity compared to device-based measures, such as accelerometers. Previous studies have reported moderate correlations (r = 0.39 to 0.53) between the PAQ-A and accelerometers, suggesting that self-reports may underestimate or overestimate actual physical activity levels [ 12 , 58 ]. This limitation is particularly relevant in adolescents, whose perception of physical activity may be influenced by factors such as memory, interpretation of questions, or the desire to provide socially acceptable responses [ 13 ]. Due to logistical and resource constraints, the PAQ-A was not complemented with device-based measures, which limits the ability to confirm the reported physical activity levels. Another limitation of the PAQ-A is its inability to capture the specific intensity, duration, and frequency of the physical activities performed, which may affect the accuracy of the proposed percentile-based cut-off points. The literature highlights that self-reported questionnaires do not provide detailed data on physical activity patterns, such as short bouts of high-intensity activity or sedentary behaviors, which are better captured by accelerometers [ 14 ]. Moreover, device-based measures are not entirely objective either, as they are subject to biases related to device use, calibration, and participant compliance [ 59 ]. Future studies should combine the PAQ-A with objective methods to validate the results and improve the accuracy of physical activity measurements. Regarding the correlations performed, the associations between physical activity variables (measured by the PAQ-A) and morphophysiological variables, such as reaction time and handgrip strength, are limited due to the self-reported nature of the data. These correlations may be influenced by variability in adolescents’ responses, which could reduce the robustness of the observed associations [ 60 ]. Additionally, the literature suggests that correlations based on self-reports may not accurately reflect the relationships between physical activity and neuromuscular outcomes, as external factors, such as environmental conditions or cultural context, may influence the reported physical activity patterns [ 61 ]. Therefore, the associations found should be interpreted with caution, and the incorporation of objective measures is recommended in future studies to strengthen the validity of these relationships. Finally, the cross-sectional design of the study limits the ability to establish causal relationships between the physical activity levels measured by the PAQ-A and the morphophysiological variables. Longitudinal studies have shown that physical activity patterns change over time in adolescents, which cannot be captured in a cross-sectional design [ 62 ]. Furthermore, contextual factors such as access to sports facilities or opportunities to engage in physical activity were not explored in depth, which could have influenced the PAQ-A results and the correlations obtained [ 15 ]. Future studies should adopt longitudinal designs and combine subjective and objective methods to provide a more comprehensive understanding of the relationships between physical activity and morphophysiological variables in adolescents. Conclusions This study enabled the classification of physical activity levels among adolescents from rural and urban areas using percentiles derived from the Physical Activity Questionnaire for Adolescents (PAQ-A), identifying low, moderate, and high ranges (25th percentile = 1.89, 50th percentile = 2.46, 75th percentile = 2.90). This percentile-based classification represents a practical approach for assessing physical activity in resource-limited settings, supported by the high internal consistency of the PAQ-A (Cronbach’s α = 0.86). However, the self-reported nature of the PAQ-A introduces limitations, such as potential memory or social desirability biases, which may affect the accuracy of the measurements. The findings revealed significant regional differences in foot morphology, particularly in plantar footprint measurements, and sex-based differences in variables such as height, handgrip strength, and simple reaction time, with large effect sizes (ES ≥ 0.80). Small to moderate correlations were identified between physical activity levels and key morphophysiological variables. Physical activity was positively associated with right-hand grip strength (r = 0.24, p ≤ 0.05) and negatively associated with simple reaction time (r = -0.37, p ≤ 0.01). These associations suggest that higher physical activity levels may be related to improved muscular strength and neuromotor responsiveness, although the cross-sectional design of the study precludes establishing causal relationships. Furthermore, reliance on the PAQ-A limits the ability to capture specific details about the intensity, duration, and frequency of physical activity, which may influence the robustness of these associations. The findings highlight the utility of the PAQ-A as an accessible tool for assessing physical activity in adolescents, particularly in settings with limited access to technologies such as accelerometry. The observed regional and sex-based differences underscore the importance of considering contextual factors, such as access to sports facilities or rural environmental demands, when designing interventions to promote physical activity. Nevertheless, the convenience sample (n = 80) and cross-sectional design limit the generalizability of the results to other populations. Future studies should incorporate objective measures, such as accelerometry, to validate the PAQ-A percentile classification and explore associations with greater precision. Additionally, longitudinal designs with larger and more diverse samples will enhance understanding of the evolution of physical activity and morphophysiological variables throughout adolescence, providing a stronger evidence base for developing physical education programs and public health policies tailored. Clinical trial Registration This study is not classified as a clinical trial. All procedures were conducted in accordance with Resolution 8430 of 1993, issued by the Ministry of Health and Social Protection of Colombia, which defines the ethical guidelines for biomedical research. In compliance with Article 28, Paragraph 1, the study was deemed to involve minimal risk. Given the nature of the study and in accordance with national regulations, formal approval by a bioethics committee was not required. Abbreviations PAQ-A Physical Activity Questionnaire for Adolescents BMI Body Mass Index METs Metabolic Equivalents of Task HCI Hernandez Corvo Index AI Asymmetry Index Declarations Supplementary information The research data are available on Figshare under the title "Categorizing Adolescent Physical Activity with PAQ-A: Morphophysiological Differences in Rural and Urban Populations" (DOI: https://doi.org/10.6084/m9.figshare.29828897). Acknowledgements The authors thank the Research and Social Outreach Office of Universidad Surcolombiana, as well as the educational institutions Juan XXIII, Los Negros and La Perdiz in the municipality of Algeciras, for their support in conducting this research. Author contributions YMDB, EDBV, and AMQ designed the methodology and conducted the research. YD and DB were responsible for data collection, tabulation, and contributed to the manuscript development. Likewise, AM participated in the statistical analysis, design, and construction of the manuscript, as well as the final approval. Funding Not applicable. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was conducted in accordance with Resolution 8430/1993 of the Ministry of Health and Social Protection of Colombia, which establishes bioethical guidelines for health research, classifying it as a minimal-risk study due to its non-invasive nature. Additionally, the ethical principles of the Declaration of Helsinki were adopted. Informed consent was obtained from parents or guardians, as well as from the students, prior to their participation in the research. Consent for publication Not applicable. Competing interests The authors declare no competing interests References Zuleta S, Poblete-Valderrama F, Monterrosa-Quintero A. Relationship between physical activity , body posture and morbidity risk in the elderly population. F100OResearch. 2025;13(1250):1–10. Shadap A. Physical Activity to Stay Fit. J Heal Allied Sci NU. 2021;11(01):08–11. Tambalis KD. 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1","display":"","copyAsset":false,"role":"figure","size":213041,"visible":true,"origin":"","legend":"\u003cp\u003eX = Medial metatarsal tracing; Y = external plantar arch\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6284518/v1/5c459a7e616fdbddf1153d48.png"},{"id":104880935,"identity":"3200f2e8-d88b-4df9-ad9a-740ea7ad0d19","added_by":"auto","created_at":"2026-03-18 09:14:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1512043,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6284518/v1/a6606283-9603-4785-a89e-7b81667b3d88.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cutoff values of the Physical Activity Questionnaire in Adolescents (PAQ-A) and their relationships with morphophysiological variables in rural and urban populations","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eThe Physical Activity Questionnaire for Adolescents (PAQ-A)\u003c/h2\u003e \u003cp\u003ePhysical activity is defined as any movement generated by muscles that involves energy expenditure, and plays a fundamental role in preventing non-communicable chronic diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The literature highlights a wide range of benefits of physical activity across all age groups, particularly emphasizing cardiovascular health, bone density, and mental well-being [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In younger populations, particularly children and adolescents, physical activity supports optimal physical development, cognitive function, and social skills [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which translates into adults with higher cognitive levels, better physical functionality, and improved psychological health [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The Physical Activity Questionnaire for Adolescents (PAQ-A) has become a widely used tool for assessing physical activity levels in youth aged 14 to 20 years old. Its application in international research has been supported by a strong internal consistency and an acceptable validity across a variety of contexts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Cutoff points above 2.73 or 2.75 have been proposed to identify adolescents who meet international physical activity recommendations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The PAQ-A has also shown moderate correlations with more objective assessment methods such as accelerometry [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], reinforcing its usefulness in settings where access to advanced technology may be limited. Translated versions, including the Spanish and Arabic adaptations, have demonstrated reliable and valid psychometric properties, supporting their use in cross-cultural studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Notably, the use of instruments such as the PAQ-A has revealed that urban adolescents exhibit higher levels of inactivity, with a non-compliance value of 33.2% on the basis of MET values [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, questionnaires such as the PAQ-A, alongside other complementary variables, serve as essential tools for assessing multiple aspects of research, including physical activity, dietary quality, and level of education.\u003c/p\u003e \u003cp\u003eDespite its strengths, the application of the PAQ-A presents several challenges due to its reliance on self-reported data. Adolescents may overestimate or underestimate their physical activity levels due to factors such as memory bias, misinterpretation of questions, or social desirability bias, which can affect the accuracy of the reported data [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, the PAQ-A does not capture specific details about the intensity, duration, or frequency of physical activities, limiting its ability to provide a comprehensive picture of activity patterns compared to objective methods like accelerometry [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The influence of external factors, such as cultural or environmental contexts, may further complicate the interpretation of PAQ-A results, as these can shape adolescents\u0026rsquo; perceptions and reporting of physical activity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These challenges highlight the importance of complementing the PAQ-A with objective measures in settings where resources permit, to enhance the validity and reliability of physical activity assessments in adolescent populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMorphophysiological variables in adolescent populations\u003c/h2\u003e \u003cp\u003eMorphophysiological variables play a significant role in human research, influencing various aspects of health and performance. Anthropometric measurements and somatotypes are essential for categorizing and identifying functional strengths or weaknesses [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this context, standardizing controllable factors such as diet, physical activity, and medication, is crucial for obtaining reliable results in physiological studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Importantly, biosocial factors significantly impact morphophysiological adaptations in urban environments [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The proper measurement of biological variables, referred to as \"physiometry\", is fundamental for ensuring replicable and efficient physiological research [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. When measurements are reliable, morphometric data become essential for athlete selection and sports specialization [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A noteworthy example is the regulation of body temperature under thermal stress, which can vary depending on morphological characteristics, intrinsic factors, diseases, and injuries [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The literature emphasizes the importance of adequate measurement properties of biological variables to ensure replicable and efficient research [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this context, upper limb speed and strength have been identified as key physical attributes associated with improved performance in young athletes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering the influence of physical activity on morphophysiological variables (defined as the science dedicated to understanding organ structure and functioning, which also studies macro and micro aspects of the human body such as footprint, response time, and body components) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] during adolescence, there are still gaps in understanding how these variables relate to rural and urban populations, as well as gender-based differences. In this context, it becomes essential to classify physical activity levels in adolescents in a practical and straightforward manner using percentiles. This approach allows for a more accurate assessment of the study population by identifying specific correlations between physical activity and the various variables presented here (morphophysiological and sociodemographic). Therefore, the present study aims, first, to categorize physical activity levels in adolescents; second, to examine the relationships between these levels and the study variables; and third and finally, to compare those variables across groups and between genders to support the development of targeted intervention strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy Type\u003c/h2\u003e \u003cp\u003eThis study follows a descriptive, correlational, and comparative design with a mixed-methods approach. The research was conducted with adolescents between August, 2023 and April, 2024. By employing body assessment techniques, physical performance evaluations, and data collection through surveys, interviews, and other tools, quantitative and qualitative methods were used to analyze physical activity levels and morphophysiological variables. The sample size was determined based on the total number of students in the school grades where the study was conducted, using a sample size calculator for finite populations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe study population consisted of students from three secondary education schools, specifically from the 10th and 11th grades, with a total of four groups, yielding a sample of eighty participants from both rural and urban areas of Algeciras, Huila, Colombia. The sample was evenly distributed, with 50% coming from rural areas and 50% coming from urban areas. In terms of gender distribution, the female sample included twenty rural and 21 urban participants, whereas the male sample included 20 rural and 19 urban participants. The sample was selected based on convenience, considering logistical factors such as the geographic proximity of the educational institutions and the public safety conditions in the study area. Convenience sampling facilitated student participation and ensured safety during the data collection process.\u003c/p\u003e\n\u003ch3\u003eInclusion criteria\u003c/h3\u003e\n\u003cp\u003eTo be eligible for the study, participants had to meet the following criteria:\u003c/p\u003e \u003cp\u003e A parent or guardian had to attend an informational meeting and sign the informed consent document.\u003c/p\u003e \u003cp\u003eStudents had to be legally enrolled in the grades where the study was conducted, as verified by their student ID.\u003c/p\u003e \u003cp\u003eThe participants had to be in good health and free of viral infections or recent injuries.\u003c/p\u003e \u003cp\u003eBoth the students and their guardians agreed to participate in the study by signing the authorization document.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExclusion Criteria\u003c/h2\u003e \u003cp\u003eParticipants with recent musculoskeletal injuries or chronic orthopedic conditions affecting movement or physical performance.\u003c/p\u003e \u003cp\u003ePresence of acute or chronic illnesses that may influence physical activity or physiological measurements (e.g., cardiovascular diseases, respiratory infections).\u003c/p\u003e \u003cp\u003eStudents currently under medication that affects neuromuscular function or physical capabilities.\u003c/p\u003e \u003cp\u003eIndividuals with cognitive or developmental disorders that could interfere with understanding or performing the assessment protocols.\u003c/p\u003e \u003cp\u003eLack of informed consent from the participant or their legal guardian.\u003c/p\u003e \u003cp\u003eAbsence from school or inability to attend the testing sessions on scheduled dates.\u003c/p\u003e \u003cp\u003eParticipants engaged in competitive sports training that could bias physical fitness outcomes.\u003c/p\u003e \u003cp\u003eThe sociodemographic, morphofunctional, and physical activity characteristics of the participants, classified by group and sex, are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003eTo collect the data, an official letter was sent to the academic coordinators of the participating schools, explaining the project's nature, scope, benefits, potential discomforts, and risks. After the letter was delivered, a meeting was arranged with the school principal, who granted permission to conduct the study. Subsequently, meetings were held with parents to explain the study details and distribute the informed consent form for their signature. Next, the physical education teacher responsible for the 10th and 11th grades was contacted and agreed to allocate class time for the study's testing protocol. Evaluations were conducted in classrooms to ensure optimal conditions for participant comfort and performance. All procedures took place in the morning, following the same protocol and using identical measurement equipment at each test site. Parental or guardian authorization was confirmed through a checklist detailing the procedures each student would undergo.\u003c/p\u003e \u003cp\u003eThe study consisted of two phases. The first phase involved administering the Physical Activity Questionnaire for Adolescents (PAQ-A), following the guidelines of Kowalski et al (2004) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. After completing the questionnaire, various morphophysiological tests were conducted. Researchers underwent prior training and conducted two pilot tests before data collection.\u003c/p\u003e\n\u003ch3\u003eVariables, Protocols, and Measurement Equipment\u003c/h3\u003e\n\u003cp\u003eA factorial design was employed in the study, encompassing six research variables:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePopulation Factor, divided into two levels (rural and urban).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSex Factor, divided into two levels (male and female).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAsymmetry Index Factor, divided into two levels (left and right).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePhysical Activity Factor, subdivided into three levels (low, moderate, and high).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHandgrip Strength Factor, subdivided into three levels (weak, normal, and strong).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFootprint Factor, subdivided into six levels (normal high arch, normal flat, strong high arch, normal, high arch, and flat).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBasic Measurements (Height, Weight, and BMI)\u003c/h2\u003e \u003cp\u003eFor the anthropometric measurements, the participants were required to arrive at 7:00 a.m. wearing sports clothes. Body weight was measured using an Omron HBF-514C digital scale, with a maximum capacity of 150 kg and an accuracy of 0.1 kg. Participants stood upright, centered on the scale, with feet evenly aligned to distribute their weight uniformly.\u003c/p\u003e \u003cp\u003eHeight was measured with a SECA 206 wall-mounted stadiometer (Hamburg, Germany), with an accuracy of 1 mm. The participants stood upright with their feet together and their hands relaxed at their sides. The evaluator applied cephalic traction to align the head according to the Frankfort horizontal plane, using their thumbs behind the ears. An assistant recorded the height by lowering the stadiometer cursor [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Each measurement was performed twice. If the degree of discrepancy exceeded 1%, a third measurement was taken, and the final value was the average of the three measurements. The body mass index (BMI) was calculated via the Quetelet equation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePhysical Activity Questionnaire for Adolescents (PAQ-A)\u003c/h2\u003e \u003cp\u003eThis questionnaire assesses weekly physical activity levels in adolescents from 9th to 12th grade, aged 14\u0026ndash;20 years old, via a Likert scale. It consists of nine items evaluating various aspects of physical activity. The questionnaire was administered electronically (via mobile phones and tablets) in urban areas, whereas a printed version was used in rural areas. The participants were informed that the questionnaire was not a performance test but rather a tool to record their actual physical activity over the past seven days. Data collection, tabulation, and analysis followed the protocol outlined by Kowalski et al (2004) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The internal consistency of the \u003cem\u003ePhysical Activity Questionnaire for Adolescents\u003c/em\u003e (PAQ-A) was evaluated in the total sample, which included participants from both urban and rural areas. The Cronbach\u0026rsquo;s alpha test yielded a value of 0.86, indicating the high reliability of the instrument. This result suggests that the responses obtained from the PAQ-A exhibit a strong internal coherence, allowing the measurement to be considered consistent and reproducible within the analyzed population. The high reliability of the questionnaire supports its suitability for assessing physical activity levels in adolescents, regardless of their geographical context.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHandgrip Strength\u003c/h2\u003e \u003cp\u003eTo assess isometric hand strength, the participants performed three attempts with each hand, with a two-minute rest interval between measurements. The participants remained seated, with the tested arm positioned at a 90\u0026deg; elbow angle. The result was the average of the three recorded values. The measurements were taken with a Trailite\u0026reg; TL-LSC 100 digital dynamometer (Germany), with a tolerance of \u0026plusmn;\u0026thinsp;0.5 kg (1 lb). The device was adjusted to fit each participant\u0026rsquo;s hand size. Both hands were tested to identify potential asymmetries in grip strength [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The strength classification followed the reference table from the dynamometer manual, defining weak grip strength as \u0026lt;\u0026thinsp;28.5 kg, normal between 28.5 To 44.3 kg, and strong as \u0026gt;\u0026thinsp;44.3 kg for individuals aged 14\u0026ndash;15 (LiteXpress GmbH; \u0026copy; Copyright TrailLite 2018).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHamstring Length (Sit-and-Reach Test)\u003c/h2\u003e \u003cp\u003eThe Acuflex 1 tester (a modified sit-and-reach box from Novel Products, Inc., USA) was used for this assessment. The participants were instructed to sit with their legs fully extended, ankles in a neutral dorsiflex position, and heels against the device. They placed one hand over the other in alignment and extended their arms while bending the trunk forward in a controlled manner along the measurement surface. Each participant performed three attempts, and the final recorded value was the average of the three, expressed in centimeters [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSimple reaction time\u003c/h2\u003e \u003cp\u003eReaction time was assessed using a lightweight, rigid 80 cm rod marked with a centimeter scale and weighted with a rubber disk at one end. The participants sat with their dominant arm extended, resting on a table, with an open hand positioned around the disk without touching it. The alignment was standardized to ensure that the disk's upper edge was level with the participant's hand. The examiner held the device vertically and released it three times, with random intervals of 2\u0026ndash;5 seconds between trials [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The reaction time was calculated via the following equation:\u003c/p\u003e \u003cp\u003e \u003cem\u003et\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=\\frac{\\sqrt{2\\text{*}\\text{h}}}{\\text{g}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003ewhere t represents the reaction time, h represents the drop distance in centimeters, and g represents gravitational acceleration (980 cm/s\u0026sup2;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFootprint measurement\u003c/h2\u003e \u003cp\u003eSeveral studies have identified an association between foot morphology, assessed through plantar footprint analysis, and physical activity levels [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The plantar footprint reflects biomechanical characteristics that may influence posture and stability, thereby affecting physical fitness. In children and adolescents, the presence of flat feet or alterations in the plantar footprint may be linked to postural deviations that limit mobility and physical performance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, evaluating the plantar footprint is essential to identify potential factors that impact physical fitness, and to guide preventive and functional improvement interventions.\u003c/p\u003e \u003cp\u003eThe quantification of the plantar footprint was conducted via the Hern\u0026aacute;ndez Corvo Index (HCI), a protocol that evaluates the arch of the foot by referencing the height of the forefoot in relation to the heel (As shown Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eKey Tracings in the Hern\u0026aacute;ndez Corvo Index\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis method has been widely used in various studies [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The participants stood in an orthostatic position on 210 mm thermal paper (fax), with their feet previously moistened with alcohol-based antibacterial gel, maintaining this posture for two minutes to record the plantar impressions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. After the established time, the footprints were outlined. With these lines, the width of the metatarsus (X) and the width of the external plantar arch (Y) were determined, and these measurements were then used to apply the following formula:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eHCI \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=\\frac{X-Y}{\\text{X}}.100\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/h2\u003e \u003cp\u003eThe result of this formula allows us to classify the foot as flat: \u0026lt; 39%, normal: (40% to \u0026lt;\u0026thinsp;59%), or high-arched\u0026thinsp;\u0026gt;\u0026thinsp;60%) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe characteristics of the participants are presented as the means and standard deviations (SD) through descriptive analysis. Data normality was assessed via the Kolmogorov‒Smirnov test, whereas variance homogeneity was verified via Levene's test. To meet the assumptions of parametric statistical analyses, data exhibiting non-normal distributions were logarithmically transformed and then re-analyzed to confirm normality before proceeding with parametric tests. To analyze the differences between groups based on the study factors, a multivariate analysis of variance (MANOVA) was performed, considering Pillai\u0026rsquo;s trace as the contrast statistic. Differences by sex were determined via a one-way analysis of variance (ANOVA), complemented by Tukey's post hoc test. Correlations between variables were analyzed via Pearson\u0026rsquo;s test, and associations were evaluated via the chi-square test. All analyses were conducted with a significant level of p\u0026thinsp;\u0026le;\u0026thinsp;0.005.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCategorization of Physical Activity Levels in Adolescents\u003c/h2\u003e \u003cp\u003eSince no standardized criteria were found in the literature for classifying physical activity levels using percentiles from the \u003cem\u003ePhysical Activity Questionnaire for Adolescents\u003c/em\u003e (PAQ-A), distinct categories were established based on the distribution of scores obtained in the sample studied. Percentiles were calculated based on the overall distribution of PAQ-A scores for the entire sample, considering a limited age range (15\u0026ndash;17 years old); therefore, ages were grouped. The analysis was conducted collectively, as no statistically significant differences were found between regional groups or by gender. Percentiles were determined using the standard empirical method through the percentile function available in the Jamovi software. The 75th percentile was used as a cut-off point to define high levels of physical activity, in line with previously reported criteria in the literature [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The descriptive percentile values for the total sample were: 25th percentile\u0026thinsp;=\u0026thinsp;1.89, 50th percentile (median)\u0026thinsp;=\u0026thinsp;2.46, and 75th percentile\u0026thinsp;=\u0026thinsp;2.90. This classification allows for a precise interpretation of physical activity levels in adolescents within the studied context.\u003c/p\u003e \u003cp\u003eThis classification allows for a more precise interpretation of the data within the specific population context, facilitating future comparisons and applications in similar studies. Although this study found a high internal consistency for the PAQ-A (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.86), as well as no significant differences by sex or group, these preliminary results suggest that the instrument is a reliable tool for assessing physical activity in adolescents from both urban and rural settings. However, to strengthen this conclusion, further reliability (test-retest) and validity analyses are recommended, including comparisons with objective measurement methods and the evaluation of metric equivalence across subgroups. It is important to interpret these findings with caution, as they are based on a specific sample and context, which may limit their generalizability to other populations or settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eRelationships Between Physical Activity Levels and Study Variables\u003c/h2\u003e \u003cp\u003eSignificant positive and negative correlations were found, ranging from small (0.1 to 0.29) to moderate (0.3 to 0.5) in magnitude, when physical activity levels were related to the study's morphophysiological variables, according to the criteria established by Hopkins (2002) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Specifically, rigth-hand grip strength showed a moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, simple reaction time was negatively correlated with physical activity levels (r = -0.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that higher physical activity is associated with a higher right-hand strength and faster reaction times.\u003c/p\u003e \u003cp\u003eThe chi-square test results indicated a significant association between sex and handgrip strength in both the right hand (χ\u0026sup2; = 14.57; p\u0026thinsp;=\u0026thinsp;0.001) and the left hand (χ\u0026sup2; = 10.52; p\u0026thinsp;=\u0026thinsp;0.005). Additionally, the relationships between types of residence (rural or urban) and plantar footprint classification (normal high arch, normal flat, strong high arch, normal, high arch, flat) were analyzed. A significant association was found for the left foot plantar footprint with residence type (χ\u0026sup2; = 11.42; p\u0026thinsp;=\u0026thinsp;0.044), suggesting that the distribution of footprint types varies depending on the type of residence.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eComparison Between Groups\u003c/h2\u003e \u003cp\u003eSignificant differences between groups were found in the plantar footprint variables for both the left and right feet (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) via a one-way ANOVA. In both cases, the effect size was small (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.20), according to Cohen\u0026acute;s, criteria (1977) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003ePhysical activity and morphophysiological characteristics of the groups.\u003c/b\u003e\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\u003eHERE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban (n\u0026thinsp;=\u0026thinsp;40)\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\u003ePAQ-A (METs)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e15.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeight (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e163.55\u0026thinsp;\u0026plusmn;\u0026thinsp;7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e162.85\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e57.21\u0026thinsp;\u0026plusmn;\u0026thinsp;7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e60.14\u0026thinsp;\u0026plusmn;\u0026thinsp;9.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight handgrip (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e31.44\u0026thinsp;\u0026plusmn;\u0026thinsp;8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e32.20\u0026thinsp;\u0026plusmn;\u0026thinsp;7.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft handgrip (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e29.26\u0026thinsp;\u0026plusmn;\u0026thinsp;7.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e30.23\u0026thinsp;\u0026plusmn;\u0026thinsp;8.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI handgrip (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrunk flexibility (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e27.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.40\u0026thinsp;\u0026plusmn;\u0026thinsp;8.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSimple reaction time (ms)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight footprint (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e51.84\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e46.76\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft foot footprint (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e53.71\u0026thinsp;\u0026plusmn;\u0026thinsp;12.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e48.49\u0026thinsp;\u0026plusmn;\u0026thinsp;8.88*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI footprint (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\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\u003eAI\u0026thinsp;=\u0026thinsp;asymmetry index; BMI\u0026thinsp;=\u0026thinsp;body mass index; PAQ-A\u0026thinsp;=\u0026thinsp;Physical Activity Questionnaire for Adolescents; * = differences between the groups (rural and urban).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in Morphophysiological and Sociodemographic Variables by Group and Gender\u003c/h2\u003e \u003cp\u003eThe results reveal notable differences in morphophysiological characteristics when disaggregated by sex and group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Regarding physical activity levels (PAQ-A), rural males reported the highest average (2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 METs), followed closely by urban males (2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59 METs). Among females, urban adolescents were more active (2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 METs) than their rural counterparts (2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46 METs). Age was consistent across all subgroups, ranging from 15.81 to 16.11 years. In terms of height, males were significantly taller than females in both settings, with rural and urban males averaging around 167.6 cm, while females averaged between 158.33 cm (urban) and 159.50 cm (rural).\u003c/p\u003e \u003cp\u003eUrban males had the highest body weight (63.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.54 kg), whereas BMI values were similar between urban males and females (~\u0026thinsp;22.7 kg/m\u0026sup2;), with rural males presenting a slightly lower BMI (20.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71 kg/m\u0026sup2;). Significant gender-based differences were observed in handgrip strength. Males showed higher values in both the right and left hands. Rural males recorded the highest grip strength (right: 37.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.77 kg; left: 35.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.67 kg), while urban females showed slightly greater values than rural females.\u003c/p\u003e \u003cp\u003eAs for trunk flexibility, rural males exhibited the highest values (28.85\u0026thinsp;\u0026plusmn;\u0026thinsp;9.08 cm), while urban males had the lowest (22.16\u0026thinsp;\u0026plusmn;\u0026thinsp;7.26 cm). Reaction time was faster among males, especially in rural areas, where boys (0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 s) significantly outperformed girls (0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 s). Regarding plantar pressure distribution, rural females displayed higher right (53.33\u0026thinsp;\u0026plusmn;\u0026thinsp;9.62%) and left (56.72\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94%) foot footprint percentages. Urban males showed the lowest values, particularly for the right foot (42.39\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79%). Differences were also noted in asymmetrical indices for plantar support and handgrip strength, although without significant patterns across all groups. In summary, while gender differences were more pronounced, particularly in strength and height, certain group-based variations emerged, especially in physical activity levels, foot morphology, and reaction time.\u003c/p\u003e \u003cp\u003eThe ANOVA results revealed significant differences in height between males and females but not within the same sex. In both cases, the effect size was large (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;1.2). A similar trend was observed in handgrip strength for both the right and left hands, where the difference between males and females showed a very large effect size (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;2.0). With respect to reaction time, significant differences were found only between rural males and females, with a large effect size (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.80). For the plantar footprint variables, differences were observed only between rural females and urban males, with a large effect size (\u003cem\u003eES\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.80). Finally, the asymmetry index of the plantar footprint differed between urban females and rural males, with effect size values exceeding 0.80. The effect size classification followed the criteria proposed by Sawilowsky (2009) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cb\u003eMorphophysiological characteristics by sex and group.\u003c/b\u003e\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\u003eHERE\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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFemale (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMale (n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban\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\u003ePAQ-A (METs)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e16.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeight (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e159.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.91*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167.60\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e167.84\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e56.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.81\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.23\u0026thinsp;\u0026plusmn;\u0026thinsp;6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e63.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e22.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight handgrip (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e25.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.77*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e37.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.71*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft handgrip (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e23.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.67*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e36.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI handgrip (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrunk flexibility (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e25.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.43\u0026thinsp;\u0026plusmn;\u0026thinsp;9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.85\u0026thinsp;\u0026plusmn;\u0026thinsp;9.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22.16\u0026thinsp;\u0026plusmn;\u0026thinsp;7.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSimple reaction time (ms)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRight foot footprint (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e53.33\u0026thinsp;\u0026plusmn;\u0026thinsp;9.62\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.71\u0026thinsp;\u0026plusmn;\u0026thinsp;8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.34\u0026thinsp;\u0026plusmn;\u0026thinsp;10.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e42.39\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeft foot footprint (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e56.72\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.31\u0026thinsp;\u0026plusmn;\u0026thinsp;6.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.70\u0026thinsp;\u0026plusmn;\u0026thinsp;14.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e46.47\u0026thinsp;\u0026plusmn;\u0026thinsp;10.66\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI footprint (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\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 \u003cem\u003eAI\u0026thinsp;=\u0026thinsp;Asymmetry Index; BMI\u0026thinsp;=\u0026thinsp;Body Mass Index; PAQ-A\u0026thinsp;=\u0026thinsp;Physical Activity Questionnaire for Adolescents. * = Differences between genders (female\u003c/em\u003e vs. \u003cem\u003emale) and not between groups (rural and urban); \u0026amp; = Differences between women and men in the rural region; % = Differences between rural women and rural men; # = Differences between rural women and urban men; f\u0026thinsp;=\u0026thinsp;Differences between urban women and urban men.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study proposed novel cut-off points for the Physical Activity Questionnaire for Adolescents (PAQ-A) and examined their relationship with morphophysiological variables in rural and urban populations. Significant differences were found between the two groups in physical activity levels, body composition, and plantar footprint patterns, suggesting that environmental context may influence both lifestyle habits and biomechanical development. Positive correlations between handgrip strength and activity levels, along with inverse associations with simple reaction time, indicate that a greater physical activity is linked to better muscular strength, neuromuscular function, and reduced asymmetries. These findings support the PAQ-A, particularly with the newly proposed cut-off values, as a practical tool for identifying physical activity levels in diverse settings. They also highlight the importance of considering morphophysiological factors when designing school and sports health interventions aimed at improving adolescent well-being and preventing injury.\u003c/p\u003e \u003cp\u003eWe have structured the discussion into different sections to facilitate the study\u0026rsquo;s comprehension, contrasting our results with the literature, proposing new lines of research, and carefully explaining our findings.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eCategorization of Physical Activity in Adolescents\u003c/h2\u003e \u003cp\u003eGiven the scientific gap in categorizing physical activity levels in adolescents from both rural and urban populations, we classified the sample, accordingly, by stratifying the levels of physical activity into low (\u0026le;\u0026thinsp;P25), moderate (P26\u0026ndash;P74), and high (\u0026ge;\u0026thinsp;P75). While no previous studies have applied this methodology specifically to the PAQ-A using percentiles, others have used devices such as accelerometers and VO₂ max to establish reliability and cutoff points for physical activity levels in adolescents, classifying them as moderate or vigorous [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Another study determined a cutoff point at a value of 2.75, where values equal to or above this threshold were classified as active and lower values as inactive [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In our search, we identified a study with similar characteristics to ours that used percentiles to classify low, moderate, and high activity levels based on the weighted scale of the nine items comprising the instrument. The classification was as follows: low (\u0026le;\u0026thinsp;2), moderate (\u0026gt;\u0026thinsp;2 and \u0026le;\u0026thinsp;3), and high (\u0026gt;\u0026thinsp;3), with the key difference being that this study was conducted on children (IPAQ-C) rather than adolescents [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the findings of this study, and using the novel percentile-based classification, slightly higher physical activity values were observed among adolescents from urban areas (2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67) as compared to their rural peers (2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58), although all values fell within the same moderate physical activity range defined by the 74th percentile (\u0026gt;\u0026thinsp;1.89 to \u0026lt;\u0026thinsp;2.90). Similarly, urban adolescent girls recorded higher average scores (2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74) than those from rural areas (2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46). However, these differences were not statistically significant, as indicated by the MANOVA (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and should therefore be interpreted with caution. These variations may reflect trends associated with contextual factors such as access to recreational spaces, sociocultural differences, or domestic workload, as reported in previous studies [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, rural boys displayed physical activity levels (2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60) that were similar to those of their urban counterparts (2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59), with both groups being within the moderate activity range. The smaller difference between rural and urban boys could be attributed to the nature of their daily activities, where rural adolescents may compensate for the lower availability of sports facilities with physical activities related to their environment, such as agricultural work or active transportation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. From an epidemiological perspective, the reported values suggest that a considerable percentage of adolescents in both contexts are within the moderate physical activity range (P26-P74), whereas a smaller proportion reach higher levels (\u0026ge;\u0026thinsp;P75). This aligns with research that indicates a global trend of insufficient physical activity among adolescents, particularly in regions with inequalities in infrastructure and exercise promotion programs [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eSignificant Relationships Between Physical Activity and Study Variables\u003c/h2\u003e \u003cp\u003eThis section presents the significant relationships identified between physical activity levels and key variables in our study, specifically handgrip strength and reaction time. These findings contribute to a better understanding of how physical activity may be associated with neuromuscular performance indicators in adolescents. The analysis aims to highlight potential patterns that could inform targeted interventions to enhance both physical fitness and motor function in this population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eHandgrip Strength\u003c/h2\u003e \u003cp\u003eFindings in the literature regarding differences in handgrip strength between urban and rural adolescents are mixed. Some studies have shown that rural youth have a greater handgrip strength, with values of 32.3 vs. 25.6 kg in Spain [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]; 27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 vs. 25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 kg in Macedonia [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]; and a study in Kosovo with similar results to ours, where differences were found by gender but not by setting [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Moreover, other studies reported no significant differences [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Gender differences were consistent across all studies, with boys demonstrating a greater handgrip strength than girls [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Factors influencing handgrip strength include age, muscle mass, body composition, and lifestyle [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These results highlight the complexity of environmental, demographic, and physiological factors in evaluating handgrip strength among adolescents from different regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eSimple reaction time\u003c/h2\u003e \u003cp\u003eSeveral studies have indicated that age significantly influences reaction time, with a trend toward faster times in older adolescents [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Additionally, significant sex differences have been reported, with generally faster reaction times in males than in females, a finding that is consistent with the results of this study [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The relevance of this variable lies in its positive correlation with attention capacity and hand\u0026ndash;eye reaction time in both sexes [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Moreover, laterality has been shown to influence reaction time, with faster responses in the dominant hand [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In the academic field, better school performance has been associated with optimized reaction times, suggesting a link between cognitive processes and response speed [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifferences in Variables According to Group and Gender\u003c/h3\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eFootprint analysis\u003c/h2\u003e \u003cp\u003eDespite significant differences in foot type among participants based on their region, the values obtained were within the normal ranges (40 to 54.9) as compared to the Plantar Footprint Index (PFI) (see Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Previous studies have reported similar values in populations with comparable characteristics, although different footprint analysis equations, such as Clarke\u0026rsquo;s, have been used. The evidence suggests that during childhood, there is a greater prevalence of flat feet (54.7% in boys and 42.7% in girls), whereas in adolescence, there is a trend towards a normal plantar structure (29.8% in boys and 35.7% in girls) across different regions [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Other studies have indicated that rural children tend to have higher arches, narrower midfoot areas, and lower forefoot pressures than urban children. When adjusting for weight, rural children presented approximately 22% lower forefoot pressures and 5% lower midfoot pressures [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn explanation for these differences between urban and rural populations could be related to footwear type, frequency of shoe usage, lifestyle, and nutritional habits. In remote regions, greater challenges exist in meeting footwear needs, conducting body assessments, and ensuring mobility due to longer distances [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe use of the Physical Activity Questionnaire for Adolescents (PAQ-A) as the primary tool to assess physical activity levels presents several limitations. Although the PAQ-A demonstrated high internal consistency in this study (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.86), self-reported instruments like this questionnaire have limited reliability and validity compared to device-based measures, such as accelerometers. Previous studies have reported moderate correlations (r\u0026thinsp;=\u0026thinsp;0.39 to 0.53) between the PAQ-A and accelerometers, suggesting that self-reports may underestimate or overestimate actual physical activity levels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. This limitation is particularly relevant in adolescents, whose perception of physical activity may be influenced by factors such as memory, interpretation of questions, or the desire to provide socially acceptable responses [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Due to logistical and resource constraints, the PAQ-A was not complemented with device-based measures, which limits the ability to confirm the reported physical activity levels.\u003c/p\u003e \u003cp\u003eAnother limitation of the PAQ-A is its inability to capture the specific intensity, duration, and frequency of the physical activities performed, which may affect the accuracy of the proposed percentile-based cut-off points. The literature highlights that self-reported questionnaires do not provide detailed data on physical activity patterns, such as short bouts of high-intensity activity or sedentary behaviors, which are better captured by accelerometers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, device-based measures are not entirely objective either, as they are subject to biases related to device use, calibration, and participant compliance [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Future studies should combine the PAQ-A with objective methods to validate the results and improve the accuracy of physical activity measurements.\u003c/p\u003e \u003cp\u003eRegarding the correlations performed, the associations between physical activity variables (measured by the PAQ-A) and morphophysiological variables, such as reaction time and handgrip strength, are limited due to the self-reported nature of the data. These correlations may be influenced by variability in adolescents\u0026rsquo; responses, which could reduce the robustness of the observed associations [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Additionally, the literature suggests that correlations based on self-reports may not accurately reflect the relationships between physical activity and neuromuscular outcomes, as external factors, such as environmental conditions or cultural context, may influence the reported physical activity patterns [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Therefore, the associations found should be interpreted with caution, and the incorporation of objective measures is recommended in future studies to strengthen the validity of these relationships.\u003c/p\u003e \u003cp\u003eFinally, the cross-sectional design of the study limits the ability to establish causal relationships between the physical activity levels measured by the PAQ-A and the morphophysiological variables. Longitudinal studies have shown that physical activity patterns change over time in adolescents, which cannot be captured in a cross-sectional design [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Furthermore, contextual factors such as access to sports facilities or opportunities to engage in physical activity were not explored in depth, which could have influenced the PAQ-A results and the correlations obtained [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Future studies should adopt longitudinal designs and combine subjective and objective methods to provide a more comprehensive understanding of the relationships between physical activity and morphophysiological variables in adolescents.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study enabled the classification of physical activity levels among adolescents from rural and urban areas using percentiles derived from the Physical Activity Questionnaire for Adolescents (PAQ-A), identifying low, moderate, and high ranges (25th percentile = 1.89, 50th percentile = 2.46, 75th percentile = 2.90). This percentile-based classification represents a practical approach for assessing physical activity in resource-limited settings, supported by the high internal consistency of the PAQ-A (Cronbach’s α = 0.86). However, the self-reported nature of the PAQ-A introduces limitations, such as potential memory or social desirability biases, which may affect the accuracy of the measurements. The findings revealed significant regional differences in foot morphology, particularly in plantar footprint measurements, and sex-based differences in variables such as height, handgrip strength, and simple reaction time, with large effect sizes (ES ≥ 0.80).\u003c/p\u003e\n\u003cp\u003eSmall to moderate correlations were identified between physical activity levels and key morphophysiological variables. Physical activity was positively associated with right-hand grip strength (r = 0.24, p ≤ 0.05) and negatively associated with simple reaction time (r = -0.37, p ≤ 0.01). These associations suggest that higher physical activity levels may be related to improved muscular strength and neuromotor responsiveness, although the cross-sectional design of the study precludes establishing causal relationships. Furthermore, reliance on the PAQ-A limits the ability to capture specific details about the intensity, duration, and frequency of physical activity, which may influence the robustness of these associations.\u003c/p\u003e\n\u003cp\u003eThe findings highlight the utility of the PAQ-A as an accessible tool for assessing physical activity in adolescents, particularly in settings with limited access to technologies such as accelerometry. The observed regional and sex-based differences underscore the importance of considering contextual factors, such as access to sports facilities or rural environmental demands, when designing interventions to promote physical activity. Nevertheless, the convenience sample (n = 80) and cross-sectional design limit the generalizability of the results to other populations. Future studies should incorporate objective measures, such as accelerometry, to validate the PAQ-A percentile classification and explore associations with greater precision. Additionally, longitudinal designs with larger and more diverse samples will enhance understanding of the evolution of physical activity and morphophysiological variables throughout adolescence, providing a stronger evidence base for developing physical education programs and public health policies tailored.\u003c/p\u003e\n\u003cdiv id=\"Sec34\"\u003e\n \u003ch2\u003eClinical trial Registration\u003c/h2\u003e\n \u003cp\u003eThis study is not classified as a clinical trial. All procedures were conducted in accordance with Resolution 8430 of 1993, issued by the Ministry of Health and Social Protection of Colombia, which defines the ethical guidelines for biomedical research. In compliance with Article 28, Paragraph 1, the study was deemed to involve minimal risk. Given the nature of the study and in accordance with national regulations, formal approval by a bioethics committee was not required.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePAQ-A\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Physical Activity Questionnaire for Adolescents\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body Mass Index\u003c/p\u003e\n\u003cp\u003eMETs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Metabolic Equivalents of Task\u003c/p\u003e\n\u003cp\u003eHCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Hernandez Corvo Index\u003c/p\u003e\n\u003cp\u003eAI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Asymmetry Index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research data are available on Figshare under the title \u0026quot;Categorizing Adolescent Physical Activity with PAQ-A: Morphophysiological Differences in Rural and Urban Populations\u0026quot; (DOI: https://doi.org/10.6084/m9.figshare.29828897).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Research and Social Outreach Office of Universidad Surcolombiana, as well as the educational institutions Juan XXIII, Los Negros and La Perdiz in the municipality of Algeciras, for their support in conducting this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYMDB, EDBV, and AMQ designed the methodology and conducted the research. YD and DB were responsible for data collection, tabulation, and contributed to the manuscript development. Likewise, AM participated in the statistical analysis, design, and construction of the manuscript, as well as the final approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with Resolution 8430/1993 of the Ministry of Health and Social Protection of Colombia, which establishes bioethical guidelines for health research, classifying it as a minimal-risk study due to its non-invasive nature. Additionally, the ethical principles of the Declaration of Helsinki were adopted. Informed consent was obtained from parents or guardians, as well as from the students, prior to their participation in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZuleta S, Poblete-Valderrama F, Monterrosa-Quintero A. Relationship between physical activity , body posture and morbidity risk in the elderly population. F100OResearch. 2025;13(1250):1\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eShadap A. Physical Activity to Stay Fit. J Heal Allied Sci NU. 2021;11(01):08\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eTambalis KD. 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Relationship between Attention Capacity and Hand\u0026ndash;Eye Reaction Time in Adolescents between 15 and 18 Years of Age. Int J Environ Res Public Health. 2022;19(17). \u003c/li\u003e\n\u003cli\u003eTaskin C. The Visual and Auditory Reaction Time of Adolescents with Respect to Their Academic Achievements. J Educ Train Stud. 2016;4(3):202\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eKalyanshetti SB, Vastrad BC. Effect of age and gender on visual, auditory and tactile reaction time in normal subjects. Biomed. 2012;32(2):217\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eEcharri JJ, Forriol F. Desarrollo de la morfolog\u0026iacute;a de la huella plantar en ni\u0026ntilde;os congole\u0026ntilde;os y su relaci\u0026oacute;n con el uso de calzado. Rev Ortop y Traumatol [Internet]. 2003;47(6):395\u0026ndash;9. Available from: http://dx.doi.org/10.1016/S1888-4415(03)76143-8\u003c/li\u003e\n\u003cli\u003eBlessy T, Yuvraj L, Rajani P. 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Physical activity change during adolescence: A systematic review and a pooled analysis. Int J Epidemiol. 2011;40(3):685\u0026ndash;98. \u003c/li\u003e\n\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":"Exercise, Body composition, Performance, Adolescents, Physical activity","lastPublishedDoi":"10.21203/rs.3.rs-6284518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6284518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAssessing physical activity levels and their associations with morphophysiological variables is crucial for promoting adolescent health and guiding interventions. This study aimed to: (a) categorize physical activity levels using percentiles from the Physical Activity Questionnaire for Adolescents (PAQ-A), (b) compare morphophysiological variables (grip strength, reaction time, foot morphology) across genders and rural/urban settings, and (c) analyze correlations between physical activity and these variables in adolescents.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA descriptive, correlational, and comparative study with a mixed-methods approach was conducted with 80 adolescents (15\u0026ndash;17 years) from Huila, Colombia. Physical activity was measured with the PAQ-A, and variables including right-hand grip strength, reaction time, plantar footprint, and anthropometric measurements (height, weight, BMI) were assessed. The data were analyzed using MANOVA, ANOVA, Pearson\u0026rsquo;s correlations, and chi-square tests.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePhysical activity levels were categorized into percentiles: 25th\u0026thinsp;=\u0026thinsp;1.89, 50th\u0026thinsp;=\u0026thinsp;2.46, 75th\u0026thinsp;=\u0026thinsp;2.90. Significant regional differences were found in plantar footprint (p\u0026thinsp;\u0026le;\u0026thinsp;0.05), with higher values in rural adolescents, and gender differences in height, grip strength, and reaction time (p\u0026thinsp;\u0026le;\u0026thinsp;0.05), with males outperforming females. Small to moderate correlations were observed between PAQ-A scores and grip strength (r\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;\u0026le;\u0026thinsp;0.05) and reaction time (r = -0.37, p\u0026thinsp;\u0026le;\u0026thinsp;0.01).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe PAQ-A (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.86) is reliable for categorizing adolescent physical activity. Higher activity levels may be linked to greater grip strength and faster reaction times, with gender and regional differences in foot morphology and physical performance. However, memory and social desirability biases in the PAQ-A may affect the accuracy of associations. Tailored interventions and objective measurements in future studies are needed to optimize physical fitness and physical outcomes in adolescents.\u003c/p\u003e","manuscriptTitle":"Cutoff values of the Physical Activity Questionnaire in Adolescents (PAQ-A) and their relationships with morphophysiological variables in rural and urban populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-23 15:11:51","doi":"10.21203/rs.3.rs-6284518/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":"f0a57a65-14b2-45db-91be-9042032c1289","owner":[],"postedDate":"December 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T09:12:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-23 15:11:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6284518","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6284518","identity":"rs-6284518","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}