Validity and reliability of Youth Physical Activity Questionnaire (YPAQ) among Preadolescents and Adolescents in Low-Resource Communities in Karachi, Pakistan

Validity and reliability of Youth Physical Activity Questionnaire (YPAQ) among Preadolescents and Adolescents in Low-Resource Communities in Karachi, Pakistan | 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 Validity and reliability of Youth Physical Activity Questionnaire (YPAQ) among Preadolescents and Adolescents in Low-Resource Communities in Karachi, Pakistan Shiraz Hashmi, Aysha Almas, Iqbal Azam, Khabir Ahmad, Tazeen H Jafar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8847825/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background : Physical activity (PA) during childhood is essential for healthy growth and development. Although objective devices provide accurate PA estimates, but are costly and resource-intensive, leading to reliance on subjective instruments. Such tools have not been validated among South Asian children and youth. The primary objectives of this study were to evaluate the criterion validity of the modified Youth Physical Activity Questionnaire (YPAQ) against accelerometer data considered as the gold standard for measuring moderate-to-vigorous physical activity (MVPA) and to assess test-retest reliability. Secondary objectives included identifying optimal YPAQ thresholds for MVPA based on Actigraph-derived data and examining gender differences in MVPA patterns within these communities. Methods : This cross-sectional validation study was conducted among school-going healthy children aged 9 to 14 years recruited from low-resource settings of Karachi. Participants’ physical or mental disabilities were excluded. Physical activity was objectively measured using Actigraph GT3X accelerometers worn for seven consecutive days. YPAQ was administered twice, one week apart. Criterion validity was evaluated using correlation coefficients (r), while test-retest reliability was assessed with intra-class correlation coefficients (ICCs) with corresponding 95% confidence intervals. Internal consistency was evaluated using Cronbach’s alpha. Receiver operating characteristic (ROC) curves were constructed to assess MVPA reported on the YPAQ in correspondence with Actigraph-derived measures. Results : Of the 252 enrolled children, 234 (93%) provided valid accelerometer data. The criterion validity coefficient for MVPA was modest [r = 0.37, (95% CI:0.29–0.44)]. Test-retest reliability was moderately strong [ICC = 0.73, (95% CI:0.68–0.77)], and internal consistency was good (Cronbach’s α = 0.75). The YPAQ demonstrated acceptable sensitivity, specificity, and area under the ROC curve in classifying per day of MVPA minutes. Gender-stratified analyses revealed weak criterion validity both for boys [r = 0.25, (95% CI:0.13–0.36)] and girls [r = 0.28, (95% CI:0.15–0.40)], while reliability estimates were comparable across genders. Conclusions : The modified YPAQ demonstrated modest validity and acceptable reliability in assessing MVPA among school-going children and youth in urban settings. These findings highlight the need for ongoing refinement and cultural adaptation of physical activity assessment tools to ensure accurate and contextually relevant estimates among South Asian children and youth, a population facing heightened risk of chronic diseases. Accelerometer GT3X preadolescents adolescents habitual physical activity low-resource settings community-based design validity and reliability LMICs. Figures Figure 1 Figure 2 Background Physical activity (PA) during childhood and adolescence is a key determinant of healthy growth and development and plays a critical role in promoting favorable long-term health outcomes. ( 1 ) Active behaviors established early in life are more likely to persist in adulthood. According to the Global Burden of Disease Study, insufficient PA has contributed to rising rates of chronic diseases, such as type 2 diabetes, over the past three decades. ( 2 ) Sedentary behavior significantly contributes to energy imbalance, thereby increasing the risk of being overweight and obesity among children and adolescents. ( 3 ) Childhood obesity often persists into adulthood and has emerged as a pressing global public health concern. ( 4 ) Trend analysis suggested that childhood overweight and obesity rates are expected to reach 30% globally by 2030. ( 5 ) This trend is alarming given its strong link with the persistent rise of non-communicable diseases (NCDs), particularly among children in South Asia. ( 6 ) Measuring PA is complex due to its multidimensional nature, particularly among children and adolescents. Objective tools such as accelerometers provide accurate PA assessments; however, their high cost and logistical demands limit their routine use in large-scale population based studies, especially in resource-constrained settings. ( 7 ) As a result, PA estimation in children and youth relies on subjective instruments. Standardized tools such as the Youth Physical Activity Questionnaire (YPAQ) and its short/modified variants were originally developed by UK researchers and used in major European youth studies (e.g. ALSPAC) ( 8 – 10 ), but they have not been validated in South Asian children and adolescents. Accurate assessment of PA, particularly MVPA, among children is crucial for quantifying its frequency, duration, and intensity. Such measurements are vital for monitoring population trends, elucidating dose-response relationships, and evaluating the effectiveness of interventions designed to mitigate long-term risks of chronic diseases. ( 11 ) The primary objectives of this study were to assess the criterion validity of the modified YPAQ against accelerometer data, considered as the gold standard for measuring MVPA, and to evaluate its test-retest reliability among school-aged preadolescents and adolescents in low-resource communities in Karachi, Pakistan. The secondary objectives included identifying optimal YPAQ thresholds for classifying MVPA using Actigraph data and exploring gender differences in activity patterns within these communities. Methods This cross-sectional validation study was conducted between August 2011 to April 2012 in 10 randomly selected low- to middle-income urban communities of Karachi. The study utilized a Wellcome Trust-funded project platform aimed to evaluate the effectiveness of community-based hypertension control interventions. ( 12 ) Ethical approval was obtained from the Ethical Review Committee of Aga Khan University (AKU), Pakistan. Sample size and sampling strategy: The sampling strategy has been described in detail elsewhere. ( 13 , 14 ). For the present study, all eligible children from the target households of the parent study were enumerated. From each household, one child was randomly selected, with the probability of selection weighted by household size to ensure representativeness across clusters. Sample size estimation was performed using PASS software (Version 2013; NCSS, Kaysville, UT, USA). A minimum sample size of 183 children was required to achieve 80% statistical power at a 5% significance level. This estimation was based on an anticipated effect size of ± 0.20 between the null and alternative hypotheses, assuming alternative correlation (r) and intra-class correlation coefficient (ICC) values ranging from 0.30 to 0.70 for validity and reliability, respectively. ( 15 ) A final target sample size of 202 participants was established to compensate for an expected 10% non-response rate and data loss stemming from insufficient Actigraph wear time recording. Subjective assessment of PA ​ Subjective assessment of PA was conducted using a modified version of YPAQ. This self-administered tool was originally developed by the Medical Research Council, Epidemiology Unit at the University of Cambridge, which has been validated for English adolescents and preadolescents. The YPAQ is based on the Children’s Leisure Activities Study Survey (CLASS), that records the frequency, duration, intensity, and type of PA over the past seven days. ( 16 ) It includes forty-seven activities categorized into seven broader groups: travel to school, classroom, and recess activities; household chores; leisure time; sports; sedentary behavior; and non-school-related travel, with separate recalls for weekdays and weekends. Modification and translation of the YPAQ Two bilingual experts translated the YPAQ into Urdu, and back-translation into English to ensure accuracy and conceptual equivalence. A third expert reviewed the translations to resolve discrepancies and mediated the process to ensure linguistic accuracy, conceptual clarity, and cultural relevance. Content validity was confirmed by two independent subject experts specializing in sports science and physical education. The YPAQ was culturally adapted by replacing activities and games with those common among Asian children, such as Kabaddi-Kabaddi (a vigorous tag-and-wrestling game), Pahel-dooj (a festive hopping game), Kho-kho (a fast-paced team chase game), and Pithu-garam (a dodgeball-like game involving stacked stone targets). These activities were replaced with alternatives such as rugby, skiing, snowboarding, sledging, skateboarding, and dog walking, which have similar metabolic equivalent task (MET) values to wrestling, hopscotch, and dodgeball. Household chores were disaggregated into sweeping, mopping, cooking, dishwashing, and grocery shopping to enhance data accuracy, as recommended by subject experts. Feasibility and face validity was examined by piloting the final draft on 10% of the calculated sample in a comparable setting. Based on pilot feedback, minor revisions were made to enhance linguistic clarity and ensure cultural appropriateness, while preserving the original intent of each item. The YPAQ was administered twice to capture last-week recalls: first at the time of Actigraph deployment, and again upon device retrieval following the seven-day monitoring protocol. Objective Assessment of PA: The objective assessment of PA was conducted via a triaxial accelerometer, the Actigraph GT3X (ActiGraph Corp., Pensacola, Florida, USA), a small (4.6 × 3.3 × 1.5 cm), lightweight (19 g), self-calibrated device that records motion data across three (vertical, medio-lateral, and antero-posterior) axes. The ActiGraph GT3X is a widely used instrument for measuring PA in school-aged children ( 17 ) and provides accurate estimates of energy expenditure when compared with indirect calorimetry. ( 18 ) Data collection procedure: Subjective PA was assessed using YPAQ, while objective measurements were collected with Actigraph GT3X accelerometers, worn continuously for seven days. Devices were distributed at the start and collected at the end of the monitoring period. Written informed consent was obtained from parents or guardians, and assent from children before participation. Baseline assessments included demographics, anthropometry, and blood pressure using standard protocols. Weight was measured in light clothing with a Solar Powered Digital Scale, model 1631 (Tanita Corp., Inc., Illinois, USA) and height without shoes using a portable stadiometer. Body mass index (BMI) was calculated as weight (kg) divided by height in (m) squared. Waist circumference was measured with inelastic tape at the midpoint between the iliac crest and the lower rib margin after exhalation. Overweight or obesity and central obesity were defined as age and sex-specific ≥ 85th percentile of BMI and waist circumference. ( 19 ) Blood pressure (mmHg) was measured thrice in the sitting position using a digital M6 Omron monitor (Healthcare Europe B.V., Hoofddorp, Netherlands) and pediatric cuff after 5 minutes of rest. The average of the last two of three readings was included in the analysis. Hypertension was defined as systolic or diastolic BP > 95th percentile for age, sex, and height. ( 20 ) Children who had elevated BP at the initial visit were reassessed within two weeks to confirm their hypertension status. A demonstration of Actigraph use was provided for children in the presence of parents or proxies, (Please refer to Supplementary Fig. 1 for the study flow and data collection process). The epoch length of Actigraph was set to 60 seconds, a commonly used interval in field-based studies that permits extended data collection. ( 21 ) The monitor was secured with a stretchable belt at the right midclavicular line on the waist and positioned near the body’s center of mass to ensure accurate recording of whole-body movements. ( 22 ) Parents and proxies were instructed to remove the monitor during water activities (swimming, showering, or bathing) and sleep. Daily reminders were provided by phone calls or text messages, and adherence was tracked with a daily log. Parents and children were informed about their obesity and hypertension status to facilitate further diagnosis and management, while Actigraph outputs were shared to communicate PA levels and highlight potential future health risks associated with insufficient activity. Outcome Variables The primary outcome variable for assessing criterion validity was defined as the correlation between MVPA time recorded by the Actigraph and the corresponding self-reported time minutes per week from YPAQ over seven consecutive days. Reliability of YPAQ was assessed using ICCs with 95% CIs, based on MVPA time (minutes per week) measured one week apart. Secondary outcomes included the optimal YPAQ-reported MVPA time (minutes per day) that corresponded to Actigraph-derived thresholds of 30–45 minutes per day. Differences in coefficients, expressed as correlation coefficients (r) and ICCs, were compared across all intensity levels by age, sex, weight status using both Actigraph and YPAQ data collected over the same one-week period. Data analysis: Statistical analyses were performed via Stata/SE version 16.1 for Windows (StataCorp LP, College Station, TX, USA). Actigraph data was processed and analyzed on ActiLife software version 6.2.1 (ActiGraph Corp., Pensacola, FL, USA). Measurements were considered valid if the Actigraph was worn for at least 8 hours per day for a minimum of 4 days, including one weekend day. ( 23 ) Periods of ten or more consecutive minutes of zero counts during waking hours were classified as non-wear time and excluded. Time spent in water-based activities, as reported on the YPAQ, was also excluded for criterion analysis. ( 24 ) The average time spent in each activity domain was calculated by dividing the total time at each intensity by the number of valid days. Activity intensities were categorized using established counts per minute (cpm) thresholds for children and youth: less than one hundred for sedentary, less than 2,295 for light, less than 4,012 for moderate, and 4,012 or more for vigorous. Each YPAQ activity was assigned a youth-specific MET value and categorized as sedentary (< 1.5), light (< 3), moderate (< 6), or vigorous (≥ 6). The domain-specific duration for each intensity was calculated as the total reported minutes divided by seven. Normality of continuous data was assessed before analysis. Data are expressed as means ± standard deviations (SDs) for normally distributed variables, or as medians with interquartile ranges (IQRs) for skewed data. Group differences were assessed using Student’s t -test or Mann-Whitney U test for continuous variables and chi-square or Fisher’s exact test for categorical variables. Pearson’s correlation coefficient (r) and corresponding 95% confidence intervals (CIs) were computed to assess criterion validity. Cronbach’s alpha and the intra-class correlation coefficient (ICC) with 95% CIs were computed to evaluate the consistency and reliability of repeated YPAQ measures. Within- and between-group coefficients were compared with the hypothesized benchmark values (r = 0.30 for validity and ICC = 0.70 for reliability) using Fisher’s z-transformation. ( 28 ) The accuracy of the YPAQ was evaluated using nonparametric ROC curves and corresponding AUCs, based on ActiGraph thresholds of 30 and 45 minutes per day of MVPA. Statistical significance was set at a two-tailed p-value less than 0.05. Results Of the 285 invited 9–14 years’ children, 252 (88.4%) consented, and 234 (93.0%) provided valid accelerometer data. The mean age of participants was 11.7 ± 1.6 years. Males accounted for 51.6% of the sample, reflecting an equal sex distribution (Table 1 ). Proxy respondents assisted 97% of children during the assessment. The prevalence of hypertension was 17.0% (95% CI: 12.4–21.6%), overweight or obesity 15.0% (95% CI: 10.6–19.4%), and central obesity 22.0% (95% CI: 16.9–27.1%). Girls exhibited significantly higher adiposity markers than boys, including BMI (p = 0.001), waist circumference (p = 0.007), and mid-arm circumference (p = 0.002). Most schools attended by participants (66%) did not provide planned, mandatory physical education sessions. Among those enrolled in schools with scheduled sessions, only 34% had access to at least one session per week. Table 1 Comparision of sociodemographic and clinical factors of study participants by gender, (n = 252). Characteristics All n = 252 Boys Girls * p value n = 130 (51.6%) n = 122 (48.4%) Age group (years) 0.703 9–11 n (%) 108 (42.9) 54 (41.5) 54 (44.3) 12–14 n (%) 144 (57.1) 76 (58.5) 68 (55.7) Age (years) 11.7 (± 1.6) 11.6 (± 1.6) 11.7 (± 1.5) 0.534 Level of education (grades) 0.574 ≤ 5th n (%) 163 (64.7) 86 (66.2) 77 (63.1) > 5th n (%) 89 (35.3) 44 (33.8) 45 (36.9) β PES at school 0.328 At least one per week n (%) 83 (32.9) 45 (34.6) 38 (31.1) Anthropometrics Weight (kg) 33.2 (± 9.5) 31.9 (± 9.0) 34.7 (± 9.7) 0.020 Height (cm) 142.8 (± 11.5) 142.6 (± 12.6) 143.1 (± 10.2) 0.691 £ BMI (kg/m 2 ) 16.0 (± 3.0) 15.5 (± 2.6) 16.6 (± 3.3) 0.001 Waist circumference (cm) 63.0 (± 8.4) 61.6 (± 7.5) 64.5 (± 9.1) 0.007 Mid-arm circumference (cm) 19.3 (± 3.0) 18.7 (± 2.6) 19.9 (± 3.2) 0.002 Overweight or obesity n (%) 38 (15.1) 19 (14.6) 19 (15.6) 0.931 £ Central obesity n (%) 56 (22.2) 25 (19.2) 31 (25.4) 0.238 Blood Pressure status Systolic BP (mmHg) 114.1 (± 11.0) 114.9 (± 11.6) 113.2 (± 10.3) 0.233 Diastolic BP (mmHg) 69.5 (± 8.1) 69.8 (± 8.0) 63.3 (± 8.2) 0.621 ¥ Hypertension n (%) 43 (17.1) 26 (20.0) 17 (13.9) 0.201 $ Valid days 6.7 (± 1.0) 6.6 (± 1.1) 6.8 (± 1.0) 0.399 Actigraph recorded time (hr./week) 76.9 ± 16.9 76.8 ± 18.1 77.1 ± 15.5 0.906 YPAQ reported time (hr./week) 79.4 ± 31.7 83.2 ± 34.4 75.3 ± 27.9 0.053 * The p- values relate to gender differences derived from Student’s t -test for and Chi-squared test where appropriate. $ Mean (± SD); β Physical Education Sessions. £ Age and gender specific ≥ 85th percentile of BMI (Kg/m 2 ) and waist circumference (cm). ¥ Hypertension defined as age, gender, and height specific > 95th percentile of SBP or DBP mmHg. $ Valid days: Actigraph data was considered valid if worn ≥ 8 hours/day for ≥ 4 days, including at least one weekend day. Children wore the accelerometer for an average of 6.7 ± 1.0 days, with comparable compliance between girls and boys (p = 0.399). The total PA duration demonstrated consistency between the two methods (p = 0.288) with the Actigraph mean recording hours of (76.9 ± 16.9) versus (79.4 ± 31.7) reported hours on the YPAQ. Comparison of MVPA between the YPAQ and accelerometer: The mean duration of MVPA reported on the YPAQ (1.1 ± 1.0 hours; ≈66 minutes) was significantly higher than that measured by the Actigraph (0.5 ± 0.4 hours; ≈30 minutes; p < 0.001). Most children did not achieve the recommended guideline of ≥ 60 minutes of MVPA per day. Only twenty-five children (10.7%) met this standard according to Actigraph data, compared with ninety-three children (39.7%) who self-reported meeting the desired benchmark on the YPAQ (Table 2 a). Table 2 a. Descriptive Statistics Comparing total and moderate to vigrous physical activity between YPAQ and Actigraph measures, (n = 234). Instrument YPAQ Actigraph * p value Time assessed (hrs./week) 79.4 ± 31.7 76.9 ± 16.9 0.288 Time spent in MVPA $ (mean hrs./day) 1.1 ± 1.0 0.5 ± 0.4 < 0.001 Time spent in MVPA $ (median min/day) 50.8 (21.7–93.3) 23.3 (12.7–44.7) < 0.001 ≥ 60 min of MVPA/day n (%) 93 (39.7) 25 (10.7) < 0.001 * The p- values derived from Student’s t -test for and Chi-squared test as appropriate. $ MVPA= Moderate and vigorous physical activities. Table 2 b. Descriptive Statistics Comparing total and moderate to vigrous physical activity between YPAQ and Actigraph measures by gender, (n = 234). Gender Boys Girls * p value n = 122 (52.1%) n = 112 (47.9%) Time spent in MVPA $ hrs./day): YPAQ (median (IQR) 1.09 (0.48–2.07) 0.57 (0.28-1.00) < 0.001 Actigraph (median (IQR) 0.68 (0.40–0.95) 0.24 (0.13–0.36) < 0.001 ≥ 60 min of MVPA/day : YPAQ n (%) 65 (53.3) 28 (25.0) < 0.001 Actigraph n (%) 23 (18.9) 2 (1.8) < 0.001 *The p-values relate to gender differences derived from Student’s t-test and Chi-squared test where appropriate. $ MVPA= Moderate and vigorous physical activities. YPAQ METs for MVPA: 3 to < 6 for moderate and ≥ 6 for vigorous activities; Actigraph counts/min for MVPA: 2295 to < 4012 for moderate and ≥ 4012 for vigorous activities. Criterion Coefficient for YPAQ-based MVPA relative to Actigraph Accelerometer: The criterion correlation among children was [r = 0.37; (95% CI: 0.29–0.44)], indicating a modest positive association that met the hypothesized threshold rho ρ of > 0.30 (p < 0.001). Agreement was modest for both weekdays [r = 0.35; (95% CI: 0.27–0.42)] and weekends [r = 0.33; (95% CI: 0.25–0.41)], with slightly higher correlations observed on weekdays, suggesting better agreement during structured days. In contrast, agreement for total weekly activities was very weak [r = 0.13; (95% CI: 0.03–0.23)], reflecting only a minimal positive relationship between the instruments (Table 3 a). Table 3 a. Criterion correlation coefficients (95% CIs) between YPAQ and Actigraph measured activities by intensity, weekday, and weekend days in children, (n = 234). Activity Intensity Overall r (95% CI) Weekday r (95% CI) Weekend r (95% CI) MVPA $ 0.37 (0.29–0.44)* 0.35 (0.27–0.42)* 0.33 (0.25–0.41)* Sedentary 0.12 (0.03–0.22)** 0.11 (0.01–0.20)** -0.04 (-0.14-0.06)** Light 0.03 (-0.07-0.13)** 0.02 (-0.08-0.12)** 0.07 (-0.03-0.17)** Total 0.13 (0.03–0.23)** 0.13 (0.03–0.22)** 0.05 (-0.05-0.15)** YPAQ reported activity intensities thresholds METs: <1.5 for sedentary, < 3 for light, < 6 for moderate and ≥ 6 for vigorous activities. Actigraph activity intensities thresholds counts/min: <100 for sedentary, < 2295 for light, < 4012 for moderate and ≥ 4012 for vigorous activities; $ MVPA= Moderate and vigorous physical activities. The p-values for within- and between-group differences were calculated against the hypothesized r ≥ 0.30 using Fisher’s z-transformation. Single asterisk (*) indicates coefficients significantly above the reference value; double asterisk (**) indicates coefficients significantly below the reference value. Table 3 b: Correlation coefficients (95% CIs) between YPAQ and Actigraph measured activities by gender, (n = 234) Activity Intensity Boys n = 123 r (95% CI) Girls n = 111 r (95% CI) MVPA $ 0.25 (0.13–0.36)** 0.28 (0.15–0.40)** Sedentary 0.19 (0.07–0.31)** 0.07 (-0.06-0.19)** Light 0.05 (-0.08-0.17)** 0.11 (-0.02-0.24)** Total 0.18 (0.07–0.29)** 0.10 (-0.03-0.22)** YPAQ reported activity intensities thresholds METs: <1.5 for sedentary, < 3 for light, < 6 for moderate and ≥ 6 for vigorous activities. Actigraph activity intensities thresholds counts/min: <100 for sedentary, < 2295 for light, < 4012 for moderate and ≥ 4012 for vigorous activities; $ MVPA= Moderate and vigorous physical activities. The p-values for within- and between-group differences were calculated against the hypothesized r ≥ 0.30 using Fisher’s z-transformation. Single asterisk (*) indicates coefficients significantly above the reference value; double asterisk (**) indicates coefficients significantly below the reference value. Gender Differences in PA Levels Based on the Actigraph: Boys consistently reported higher MVPA levels than girls on both the YPAQ and Actigraph measures. Median MVPA duration by YPAQ was 1.09 hours/day (IQR: 0.48–2.07) for boys and 0.57 hours/day (IQR: 0.28-1.00) for girls. The median MVPA by Actigraph, was 0.68 hours/day (IQR: 0.40–0.95) for boys compared and only 0.24 hours/day (IQR: 0.13–0.36) for girls. These differences were statistically significant across both instruments, p < 0.001 (Table 2 b). The proportion of children achieving ≥ 60 minutes of MVPA per day differed significantly by sex. According to YPAQ, 65 boys (53.3%) and 28 girls (25.0%) met the recommended threshold, whereas Actigraph data indicated that 23 boys (18.9%) and only 2 girls (1.8%) achieved the desired benchmark. Both differences were statistically significant, p < 0.001 (Table 2 b). Gender-stratified criterion analyses revealed modest and comparable coefficients for MVPA among boys [r = 0.25; 9(5% CI: 0.13–0.36)] and girls [r = 0.28; (95% CI: 0.15–0.40)], with no significant difference (p = 0.654). Both estimates fell below the hypothesized benchmark, although girls demonstrated slightly higher coefficients (Table 3 b). In addition, girls spent more time in sedentary activities and less time in light and MVPA compared with boys across all PA intensities p < 0.001 (Fig. 1 ). Age-stratified analyses indicated modest agreement for adolescents aged 12–14 years [r = 0.40; (95% CI: 0.28–0.51)] and preadolescents aged 9–11 years [r = 0.32; (95% CI: 0.16–0.46)]. Agreement was slightly higher among normal-weight children [r = 0.39; (95% CI: 0.31–0.51)] compared with overweight or obese children [r = 0.26; (95% CI: -0.08-0.56)], though these differences were not statistically significant (p values were 0.342 and 0.430 respectively) for both groups (Please see, Supplementary Table 1). The areas under the ROC curves indicated fair discrimination, with an AUC of 0.68 (95% CI: 0.62–0.74) for the 30‑minute threshold and 0.64 (95% CI: 0.58–0.71) for the 45‑minute threshold. Actigraph-derived MVPA thresholds of 30 minutes per day demonstrated 75.3% sensitivity for identifying active children and 56.9% specificity for detecting inactive children based on YPAQ responses. A trade off was evident, at the 45-minute threshold, sensitivity decreased to 59.7% while specificity increased to 68.4% (Fig. 2 ). The YPAQ overestimated MVPA by approximately 40% consistently showing a ratio of 1.4 compared to objective measures across both applied thresholds. Reliability of the YPAQ: The YPAQ demonstrated good internal consistency (Cronbach’s alpha = 0.75). Test-retest reliability for MVPA in children was strong, with an ICC of [0.73; (95% CI: 0.68–0.77)], exceeding the 0.70 threshold and indicating stable, reproducible measurement (Table 4 a). Table 4 a. Intra-class correlation coefficients (ICC) and 95% CIs of YPAQ repeat measures by intensity, week, and weekend day in children, (n = 252) Activity Intensity Weekday ICC (95% CI) Weekend ICC (95% CI) Overall ICC (95% CI) MVPA $ 0.67 (0.61–0.72)** 0.67 (0.61–0.72)** 0.73 (0.68–0.77)* Sedentary 0.75 (0.70–0.79)* 0.61 (0.55–0.66)** 0.78 (0.73–0.82)* Light 0.62 (0.57–0.67)** 0.65 (0.60–0.70)** 0.69 (0.64–0.74)** Total 0.50 (0.44–0.55)** 0.58 (0.53–0.63)** 0.62 (0.57–0.67)** YPAQ reported activity intensities thresholds METs: <1.5 for sedentary, < 3 for light, < 6 for moderate and ≥ 6 for vigorous activities. $ MVPA= Moderate and vigorous physical activities. The p-values for within- and between-group differences were calculated against the hypothesized ICC ≥ 0.70 using Fisher’s z-transformation. Single asterisk (*) indicates coefficients significantly above the reference value; double asterisk (**) indicates coefficients significantly below the reference value. Table 4 b. Intra-class correlation coefficients (ICC) and 95% CIs of YPAQ repeat measures by intensity and gender (n = 252) Activity Intensity Boys, n = 130 ICC (95% CIs) Girls, n = 122 ICC (95% CIs) MVPA $ 0.69 (0.59–0.77)** 0.71 (0.62–0.78)** Sedentary 0.79 (0.71–0.85)* 0.76 (0.68–0.82)* Light 0.73 (0.63–0.80)* 0.66 (0.55–0.74)** Total 0.60 (0.47–0.71)** 0.63 (0.51–0.72)** YPAQ reported activity intensities thresholds METs: <1.5 for sedentary, < 3 for light, < 6 for moderate and ≥ 6 for vigorous activities. $ MVPA= Moderate and vigorous physical activities. The p-values for within- and between-group differences were calculated against the hypothesized ICC ≥ 0.70 using Fisher’s z-transformation. Single asterisk (*) indicates coefficients significantly above the reference value; double asterisk (**) indicates coefficients significantly below the reference value. Reliability was comparable for weekdays and weekends, [ICC = 0.67; (95% CI: 0.61–0.72)], slightly below the weekly benchmark. Reliability was acceptable both for boys [ICC = 0.69; 95% CI: 0.59–0.77)] and girls [ICC = 0.71; (95% CI: 0.62–0.78)], with no significant difference between groups p = 0.678 (Table 4 b). Reliability for total PA was also acceptable [ICC = 0.62; (95% CI: 0.57–0.67)], though below the 0.70 threshold. Stratified analyses indicated higher reliability among adolescents [ICC = 0.79; (95% CI: 0.73–0.84)] compared with younger children [ICC = 0.66; 95% CI: 0.54–0.76)]. Reliability was also higher among normal-weight children [ICC = 0.74; (95% CI: 0.70–0.78)] than overweight children [ICC = 0.69; (95% CI: 0.51–0.82)], suggesting more stable measurement in older and normal-weight groups (Please see, Supplementary Table 2). Discussion This community-based validation study of the modified YPAQ against the Actigraph accelerometer addresses an important gap in self-reported PA assessment and provides a foundation for developing culturally tailored, context-appropriate surveillance tools. The YPAQ demonstrated moderate agreement with Actigraph-derived MVPA estimates, with a correlation coefficient of r = 0.37. These findings are consistent with previous studies conducted in high-income countries, which reported correlation coefficients ranging from 0.30 to 0.47 among similar age groups. ( 8 , 16 , 29 – 33 ) To date, no validation study has examined the standardized YPAQ against accelerometer-based measures in LMICs. However, several locally developed or adapted physical activity questionnaires in LMICs have demonstrated modest validity and acceptable reliability compared to objective measures. For instance, an Indian study employing the Madras Diabetes Research Foundation PA Questionnaire for Children (MPAQ-C) reported a moderate correlation with accelerometer-derived MVPA (r = 0.41) and moderately strong test-retest reliability (ICC = 0.77) among participants aged 10–17 years, including both children and older youth. ( 34 ) Similarly, Tanaka et al. in Japan observed modest validity between the WHO Health Behavior in School-aged Children (HBSC) questionnaire and accelerometer-based MVPA (r = 0.35) along with moderately strong reproducibility (ICC = 0.72). ( 35 ) In China, Wang et al. reported comparable validity (r = 0.36) and reliability (ICC = 0.75) for the PAQ-C among school-aged children and adolescents. ( 32 ) This study extends previous research by validating the detailed Youth PA Questionnaire (YPAQ) in an LMIC context, demonstrating comparable performance to other self-report instruments for assessing MVPA against accelerometer-derived estimates among urban school-aged children and adolescents. The observed criterion coefficient (r = 0.37) reflects a modest positive correlation, consistent with the expected benchmark of ρ ≥ 0.30. While modest, this level of agreement underscores the relevance of MVPA in the prevention of non-communicable diseases, particularly cardiovascular conditions. These findings emphasize the importance of public health promoting adequate PA among children to mitigate future NCD risk. Self-report PA assessment tools often overestimate MVPA intensity and duration due to recall errors and social desirability bias. ( 36 ) Our ROC curve analysis, which is rarely employed in validation studies ( 37 ), confirmed this trend. The YPAQ overestimated MVPA by approximately 40% at both 30‑ and 45‑minute daily thresholds in our sample (Fig. 2 ). Therefore, self-reported measurements should be interpreted with caution, and appropriate calibrations or adjustments should be considered when accelerometer-based assessments are unavailable. In contrast, total activity measured by the YPAQ demonstrated only a weak association with accelerometer-based assessment. This finding is expected, as total activity encompasses lower-intensity and sporadic movements that are difficult for children to recall accurately and are inherently more complex. ( 38 ) Gender-stratified analyses revealed modest correlations for both boys and girls, indicating limited agreement between the instruments. Trivial differences may reflect variations in activity type, intensity, or recall accuracy. Girls often participate in less sporadic but more structured activities, which are easier to report. ( 39 ) In our study, only 11% of children achieved the recommended MVPA. Girls consistently engaged in lower levels of MVPA than boys, as observed with both instruments. This pattern reflects global trends, with pooled data from 64 LMICs showing that boys are 1.6 times more likely than girls to meet recommended physical activity levels. ( 40 ) Targeted, gender-specific, and culturally sensitive strategies are therefore essential, as girls may respond differently to standard interventions and face greater long-term health risks associated with sedentary lifestyles. Questionnaire-based methods are practical and cost-effective, although they remain subject to recall bias. ( 17 ) Our age-stratified analysis indicated that preadolescents often struggle to distinguish between activity intensity levels, likely due to developmental and cognitive limitations, which may lead to misclassification. ( 8 ) Among adolescents, both under- and over-reporting of MVPA are possible, as this group may be more influenced by social-desirability bias. Children aged 9–11 years, who typically engage in light or intermittent activities, tend to underestimate or misclassify the intensity of past behaviors when recalling them. ( 41 , 42 ) (Please see, Supplementary Table 1). The YPAQ demonstrated acceptable internal consistency (Cronbach’s alpha = 0.75) and reasonable test-retest reliability for MVPA (ICC = 0.73), indicating consistent reporting among children. Reliability was higher among girls, adolescents, and normal-weight children, with ICC values within the good range. These findings suggest that the YPAQ is a reliable tool for estimating MVPA in older and normal-weight children. Self-report tools such as the YPAQ should therefore be used primarily to estimate MVPA when accelerometer data are unavailable. They are suitable for general assessments or screening purposes but lack the precision required for detailed measurement. ( 37 , 41 ) Our study offers several strengths. Its community-based design enabled validation of the detailed YPAQ against Actigraph measures in a representative sample of school-aged children and adolescents from a low-resource urban setting. Gender stratified analysis found significant differences in MVPA patterns that consistent with global data thereby supporting the focus on targeted interventions in low- and middle-income countries. We applied intensity-specific cpm thresholds and 60-second epochs, as recommended by Evenson et al. ( 25 ), to enhance classification accuracy and support regional standardization. Shorter epoch lengths (e.g., 5–15 seconds) may further improve detection of brief MVPA bursts common in children. Newer Actigraph models, such as the GT9X Link, provide high-resolution data and are valuable for pediatric physical activity research. Beyond correlation estimates, we employed ROC curve analysis to assess estimation errors for MVPA between instruments ( 43 ), a methodological approach with important implications for public health and policy. To minimize potential information bias, the same proxy respondent assisted most of the children during data collection. Most participants complied with the study protocol by wearing the Actigraph for the required duration. These findings should be interpreted with caution due to several limitations. We operated under the assumption that participants’ activity patterns would not significantly change over the course of the study. ( 44 ) The exclusive recruitment of urban school participants limits the generalizability to rural and those out-of-school children. Potential device tampering could not be monitored, which is a common challenge in community-based research. An initial Hawthorne effect ( 45 ) may occur when the Actigraph is first worn, however, activity data from the first and last days suggest this effect was minimal. Water-based activities were excluded because the Actigraph was not worn during these periods; however, newer water-resistant models (e.g., GT3X + and GT9X Link) can now capture water related activities and offer more flexible data processing. ( 22 ) Few children achieved the recommended 60 minutes of MVPA per day, so we applied lower thresholds of 30 and 45 minutes for comparison with YPAQ results. This finding aligns with evidence that effective youth activity programs include 30–45 minutes of continuous MVPA multiple times per week. ( 1 ) Our sampling strategy was based on cluster size, but the coefficients were not adjusted for clustering. The latest WHO guidelines recommend subgroup analyses to examine whether physical activity patterns and health outcomes differ by age, sex, body weight, race/ethnicity, and socioeconomic status. ( 46 ) Future research should therefore include larger, more diverse samples to enable such analyses. Selecting appropriate accelerometer cutoff points remains a methodological challenge, as thresholds vary by age and sex. Using uniform thresholds risks misclassification, underscoring the need for population-specific calibration. ( 36 ) Finally, the high cost of Actigraph devices (approximately USD 2,000–3,000 per unit, including software) limits their feasibility in large-scale studies, particularly in low-resource settings. (6, 47) Evaluating affordable, validated wearable devices is therefore critical to support physical activity research in LMICs. Conclusion Our study concludes that the YPAQ has modest validity and moderately strong reliability for assessing MVPA among school-going adolescents and preadolescents in low- to middle-income urban communities in Pakistan. Despite its utility for measuring MVPA the YPAQ exhibits limited correlation with total PA and should therefore not be used for a comprehensive assessment of overall PA. For greater accuracy, both MVPA and total PA should be measured with accelerometers, though high cost prevents large scale deployment of this gold standard. Future research should prioritize the development of culturally relevant and reliable tools to accurately capture PA patterns among South Asian children and youth a population with an elevated susceptibility to chronic diseases. Abbreviations AUC: Area Under the Curve METs: Metabolic Equivalent Task Scores MVPA: Moderate to Vigorous Physical Activity ROC: Receiver operating characteristic curve YPAQ: Youth Physical Activity Questionnaire Declarations Ethics approval and consent to participate The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Ethical Review Committee (ERC) of Aga Khan University, Pakistan (1643-CHS-ERC-10). Written informed consent was obtained from the parents or guardians, and assent was obtained from the participating children. Consent for publication All authors consented to the publication of this article. Availability of data and materials The datasets used in the current study are available from the corresponding author upon reasonable request. Competing interests The authors of this manuscript have no potential, perceived, or real conflicts of interest. Funding This work is supported by the Wellcome Trust, UK, through the M.Sc. Fellowship grant # 090680/Z/09/Z awarded to Shiraz Hashmi under the supervision of Professor Tazeen H. Jafar. The design, conduct, analysis, interpretation, and presentation of the data was the responsibility of the authors, with no involvement from the funding agency. Authors' contributions SH conducted the study, coordinated the data collection, performed the preliminary analysis, drafted the initial manuscript, and carried out subsequent revisions. IQ and KA contributed to the study design, reviewed the analytic procedures, assisted in data analysis, data interpretation and critically reviewed the manuscript. TJ conceptualized and mentored the study, critically reviewed the analytic procedures and interpretations, and critically reviewed the manuscript. AA reviewed the final draft of the manuscript for important intellectual content. All the authors read and approved the final version of the manuscript. Acknowledgments We thank the translators and subject matter experts for their contributions in adapting and refining the YPAQ. We also appreciate the field and administrative staff for their support in data collection and study implementation. Most importantly, we are grateful to the children and their parents for their time and cooperation, which made this study possible. Implications and Contributions Novelty: This community-based study is the first in the region to validate a standardized Youth Physical Activity Questionnaire (YPAQ) against objective accelerometer-based measures in school aged children and adolescents facilitating the development of culturally appropriate surveillance tools. Global Relevance : The modified YPAQ demonstrated modest criterion validity for assessing moderate-to-vigorous physical activity (MVPA) consistent with findings from international studies, which enhances global knowledge base on physical activity assessment. Caution: Consistent withother self-report instruments, the YPAQ overestimates MVPA. Its use requires caution, and calibration or adjustment factors are recommended when objective devices (such as accelerometer) are unavailable. Future steps: Future efforts should prioritize the robust development of culturally validated physical activity instruments accounting for key demographic differences (age, sex, and body weight status) to design effective targeted interventions for population most vulnerable to inactivity. References Strong WB, Malina RM, Blimkie CJ, Daniels SR, Dishman RK, Gutin B, et al. Evidence based physical activity for school-age youth. J Pediatr. 2005;146(6):732-7.https://doi.org/10.1016/j.jpeds.2005.01.055 Yang L, Xie D, Liu F, Lin J, Lin X, Chen Y, et al. Global and Regional Burden of Type 2 Diabetes Mellitus Attributable to Low Physical Activity From 1990 to 2021. J Diabetes. 2025;17(1):e70043.https://doi.org/10.1111/1753-0407.70043 Jafar TH, Qadri Z, Islam M, Hatcher J, Bhutta ZA, Chaturvedi N. Rise in childhood obesity with persistently high rates of under-nutrition among urban school-aged Indo-Asian children. Arch Dis Child. 2008;93(5):373-8.https://doi.org/10.1136/adc.2007.125641 Geserick M, Vogel M, Gausche R, Lipek T, Spielau U, Keller E, et al. Acceleration of BMI in early childhood and risk of sustained obesity. N Engl J Med. 2018;379(14):1303-12.https://doi.org/10.1056/NEJMoa1803527 Gao L, Peng W, Xue H, Wu Y, Zhou H, Jia P, et al. Spatial–temporal trends in global childhood overweight and obesity from 1975 to 2030: a weight mean center and projection analysis of 191 countries. Global Health. 2023;19(1):53.https://doi.org/10.1186/s12992-023-00954-5 Misra A, Khurana L. The metabolic syndrome in South Asians: epidemiology, determinants, and prevention. Metab Syndr Relat Disord. 2009;7(6):497-514.https://doi.org/10.1186/s12992-023-00954-5 Lee YS, Komar J, Chia MYH. Physical Activity Measurement Methodologies: A Systematic Review in the Association of South East Asian Nations (ASEAN). Sports (Basel). 2021;9(5).https://doi.org/10.3390/sports9050069 McCrorie PRW, Perez A, Ellaway A. The validity of the Youth Physical Activity Questionnaire in 12-13-year-old Scottish adolescents. BMJ Open Sport Exerc Med. 2016;2(1):e000163.https://doi.org/10.1136/bmjsem-2016-000163 Corder K, van Sluijs EM, Wright A, Whincup P, Wareham NJ, Ekelund U. Is it possible to assess free-living physical activity and energy expenditure in young people by self-report? Am J Clin Nutr. 2009;89(3):862-70.https://doi.org/10.3945/ajcn.2008.26739 Brooke HL, Corder K, Griffin SJ, Ekelund U, van Sluijs EM. More of the same or a change of scenery: an observational study of variety and frequency of physical activity in British children. BMC Public Health. 2013;13:761.https://doi.org/10.1186/1471-2458-13-761 Nelson DE, Moon RW, Holtzman D, Smith P, Siegel PZ. Patterns of health risk behaviors for chronic disease: a comparison between adolescent and adult American Indians living on or near reservations in Montana. Journal of adolescent health. 1997;21(1):25-32.https://doi.org/10.1016/s1054-139x(96)00274-1 Jafar TH, Hatcher J, Poulter N, Islam M, Hashmi S, Qadri Z, et al. Community-based interventions to promote blood pressure control in a developing country: a cluster randomized trial. Ann Intern Med. 2009;151(9):593-601.https://doi.org/10.7326/0003-4819-151-9-200911030-00004 Islam M, Jafar TH, Wood AR, De Silva NM, Caulfield M, Chaturvedi N, et al. Multiple genetic variants explain measurable variance in type 2 diabetes-related traits in Pakistanis. Diabetologia. 2012;55(8):2193-204.https://doi.org/10.1007/s00125-012-2560-y Jafar TH, Qadri Z, Chaturvedi N. Coronary artery disease epidemic in Pakistan: more electrocardiographic evidence of ischaemia in women than in men. Heart. 2008;94(4):408-13.https://doi.org/10.1136/hrt.2007.120774 Walter SD, Eliasziw M, Donner A. Sample size and optimal designs for reliability studies. Stat Med. 1998;17(1):101-10.https://doi.org/10.1002/(sici)1097-0258(19980115)17:13.0.co;2-e Telford A, Salmon J, Jolley D, Crawford D. Reliability and validity of physical activity questionnaires for children: The Children’s Leisure Activities Study Survey (CLASS). Pediatric exercise science. 2004;16(1):64-78.https://doi.org/10.1123/pes.16.1.64 Jurado-Castro JM, Gil-Campos M, Llorente-Cantarero FJ. Methods recently used for the assessment of physical activity in children and adolescents. Curr Opin Clin Nutr Metab Care. 2022;25(5):298-303.https://doi.org/10.1097/mco.0000000000000847 Crouter SE, Horton M, Bassett Jr DR. Validity of ActiGraph child-specific equations during various physical activities. Med Sci Sports Exerc. 2013;45(7):1403.https://doi.org/10.1249/MSS.0b013e318285f03b Almas A, Jafar TH. Adiposity and blood pressure in South Asian children and adolescents in Karachi. Am J Hypertens. 2011;24(8):876-80.https://doi.org/10.1038/ajh.2011.67 Falkner B, Daniels SR. Summary of the fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Hypertension. 2004;44(4):387-8.https://doi.org/10.1161/01.HYP.0000143545.54637.af Riddoch CJ, Mattocks C, Deere K, Saunders J, Kirkby J, Tilling K, et al. Objective measurement of levels and patterns of physical activity. Arch Dis Child. 2007;92(11):963-9.https://doi.org/10.1136/adc.2006.112136 Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37(11 Suppl):S531-43.https://doi.org/10.1249/01.mss.0000185657.86065.98 Trost SG, Pate RR, Freedson PS, Sallis JF, Taylor WC. Using objective physical activity measures with youth: how many days of monitoring are needed? Med Sci Sports Exerc. 2000;32(2):426-31.https://doi.org/10.1097/00005768-200002000-00025. Riddoch CJ, Bo Andersen L, Wedderkopp N, Harro M, Klasson-Heggebo L, Sardinha LB, et al. Physical activity levels and patterns of 9 -15 years-old European children. Med Sci Sports Exerc. 2004;36(1):86-92.https://doi.org/10.1249/01.MSS.0000106174.43932.92. Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26(14):1557-65.https://doi.org/10.1080/02640410802334196 Ridley K, Ainsworth BE, Olds TS. Development of a compendium of energy expenditures for youth. Int J Behav Nutr Phys Act. 2008;5:45.https://doi.org/10.1186/1479-5868-5-45 Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32(9 Suppl):S498-504.https://doi.org/10.1097/00005768-200009001-00009 Fisher RA. On the" Probable Error" of a Coefficient of Correlation Deduced from a Small Sample. Metron. 1921;1:3-32 Janz KF, Lutuchy EM, Wenthe P, Levy SM. Measuring activity in children and adolescents using self-report: PAQ-C and PAQ-A. Med Sci Sports Exerc. 2008;40(4):767-72.https://doi.org/10.1249/MSS.0b013e3181620ed1 Lachat CK, Verstraeten R, Khanh le NB, Hagstromer M, Khan NC, Van Ndo A, et al. Validity of two physical activity questionnaires (IPAQ and PAQA) for Vietnamese adolescents in rural and urban areas. Int J Behav Nutr Phys Act. 2008;5:37.https://doi.org/10.1186/1479-5868-5-37 Lubans DR, Sylva K, Osborn Z. Convergent Validity and Test Retest Reliability of the Oxford Physical Activity Questionnaire for Secondary School Students. Behavior Change. 2008;25(01):23-34.https://doi.org/10.1375/bech.25.1.23 Wang JJ, Baranowski T, Lau WP, Chen TA, Pitkethly AJ. Validation of the Physical Activity Questionnaire for Older Children (PAQ-C) among Chinese Children. Biomed Environ Sci. 2016;29(3):177-86.https://doi.org/10.3967/bes2016.022 Marasso D, Lupo C, Collura S, Rainoldi A, Brustio PR. Subjective versus objective measure of physical activity: a systematic review and meta-analysis of the convergent validity of the physical activity questionnaire for children (PAQ-C). Int J Environ Res Public Health. 2021;18(7):3413.https://doi.org/10.3390/ijerph18073413 Mehreen TS, Ranjani H, Anitha C, Jagannathan N, Pratt M, Mohan V, et al. Reliability and Validity of a Physical Activity Questionnaire for Indian Children and Adolescents. Indian Pediatr. 2020;57(8):707-11.https://doi.org/10.1007/s13312-020-1912-9 Tanaka C, Kyan A, Takakura M, Olds T, Schranz N, Tanaka S. Validation of the Physical Activity Questions in the World Health Organization Health Behavior in School-Aged Children Survey Using Accelerometer Data in Japanese Children and Adolescents. J Phys Act Health. 2021;18(2):151-6.https://doi.org/10.1123/jpah.2019-0671 Ekelund U, Tomkinson G, Armstrong N. What proportion of youth are physically active? Measurement issues, levels and recent time trends. Br J Sports Med. 2011;45(11):859-65.https://doi.org/10.1136/bjsports-2011-090190 Chinapaw MJ, Mokkink LB, van Poppel MN, van Mechelen W, Terwee CB. Physical activity questionnaires for youth: a systematic review of measurement properties. Sports Med. 2010;40(7):539-63.https://doi.org/0.2165/11530770-000000000-00000 Marasso D, Lupo C, Collura S, Rainoldi A, Brustio PR. Subjective versus Objective Measure of Physical Activity: A Systematic Review and Meta-Analysis of the Convergent Validity of the Physical Activity Questionnaire for Children (PAQ-C). Int J Environ Res Public Health. 2021;18(7).https://doi.org/10.3390/ijerph18073413 Telford RM, Telford RD, Olive LS, Cochrane T, Davey R. Why Are Girls Less Physically Active than Boys? Findings from the LOOK Longitudinal Study. PLoS One. 2016;11(3):e0150041.https://doi.org/10.1371/journal.pone.0150041 Ricardo LIC, Wendt A, Costa CDS, Mielke GI, Brazo-Sayavera J, Khan A, et al. Gender inequalities in physical activity among adolescents from 64 Global South countries. J Sport Health Sci. 2022;11(4):509-20.https://doi.org/10.1016/j.jshs.2022.01.007 Helmerhorst HJ, Brage S, Warren J, Besson H, Ekelund U. A systematic review of reliability and objective criterion-related validity of physical activity questionnaires. Int J Behav Nutr Phys Act. 2012;9:103.https://doi.org/10.1186/1479-5868-9-103 Neilson HK, Robson PJ, Friedenreich CM, Csizmadi I. Estimating activity energy expenditure: how valid are physical activity questionnaires? Am J Clin Nutr. 2008;87(2):279-91.https://doi.org/10.1093/ajcn/87.2.279 Jago R, Zakeri I, Baranowski T, Watson K. Decision boundaries and receiver operating characteristic curves: new methods for determining accelerometer cut points. J Sports Sci. 2007;25(8):937-44.https://doi.org/10.1080/02640410600908027 Goran MI. Measurement issues related to studies of childhood obesity: assessment of body composition, body fat distribution, physical activity, and food intake. Pediatrics. 1998;101(3 Pt 2):505-18.https://doi.org/10.1542/peds.101.S2.505 Corder K, Ekelund U, Steele RM, Wareham NJ, Brage S. Assessment of physical activity in youth. J Appl Physiol. 2008;105(3):977-87.https://doi.org/10.1152/japplphysiol.00094.2008 Chaput JP, Willumsen J, Bull F, Chou R, Ekelund U, Firth J, et al. 2020 WHO guidelines on physical activity and sedentary behaviour for children and adolescents aged 5-17 years: summary of the evidence. Int J Behav Nutr Phys Act. 2020;17(1):141.https://doi.org/10.1186/s12966-020-01037-z Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigure1.tif Supplementary Figure 1. Flow diagram showing recruitment process for the study participants. Supplementaryfile1.docx Supplementaryfile2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 23 Mar, 2026 Editor assigned by journal 20 Feb, 2026 Submission checks completed at journal 20 Feb, 2026 First submitted to journal 11 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8847825","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611042482,"identity":"9f510dbb-f555-4f6f-8b74-3f16ece72774","order_by":0,"name":"Shiraz Hashmi","email":"","orcid":"","institution":"Aga Khan University","correspondingAuthor":false,"prefix":"","firstName":"Shiraz","middleName":"","lastName":"Hashmi","suffix":""},{"id":611042485,"identity":"ac5c0061-9848-4564-8cb1-8026bc294473","order_by":1,"name":"Aysha Almas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie2OsQrCMBCGTwp1OahjSkt9AiEixEHQVyk4dMmgm+Dg2KUP4GP4CIWCXQKucatLXDLo5uBgOrmlugnmgzvu4D7uB3A4fhO/bbGpXgNlO6efKWjKo18rPvlICfaZah4wx1Feq81DVBD0ObUqRPLpuIAlMsHZuZAVhIW2KyC5TxA8ZCX3Jd4qoLLjy1BmKnzCDtnpqtZPoyy6FCpTFiFUyMzgoQlGSYcyFppFMa2NoidRLDIkQq2sSlKbYHqzTdgpu9z1cZYE+fJgVWCQtvHeO9rPW4Ky+8bhcDj+nBcfE0d/isRqEgAAAABJRU5ErkJggg==","orcid":"","institution":"Aga Khan University","correspondingAuthor":true,"prefix":"","firstName":"Aysha","middleName":"","lastName":"Almas","suffix":""},{"id":611042486,"identity":"81e94b0a-576b-4387-9310-e65e2ddfed87","order_by":2,"name":"Iqbal Azam","email":"","orcid":"","institution":"Aga Khan University","correspondingAuthor":false,"prefix":"","firstName":"Iqbal","middleName":"","lastName":"Azam","suffix":""},{"id":611042489,"identity":"30eb92b1-8dd0-4553-b04f-79b45bbfc3bd","order_by":3,"name":"Khabir Ahmad","email":"","orcid":"","institution":"Aga Khan University, King Khaled Eye Specialist Hospital","correspondingAuthor":false,"prefix":"","firstName":"Khabir","middleName":"","lastName":"Ahmad","suffix":""},{"id":611042491,"identity":"d174b47d-066b-4d50-98a6-a83f206419e1","order_by":4,"name":"Tazeen H Jafar","email":"","orcid":"","institution":"Duke University","correspondingAuthor":false,"prefix":"","firstName":"Tazeen","middleName":"H","lastName":"Jafar","suffix":""}],"badges":[],"createdAt":"2026-02-11 06:25:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8847825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8847825/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105410608,"identity":"bc12af4f-976d-4b1b-a872-a45ab3b0e09b","added_by":"auto","created_at":"2026-03-25 17:17:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":121578,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of total time spent in different physical activity intensity categories, as assessed by the Actigraph, stratified by sex (p \u0026lt;0.001 for all intensity categories).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8847825/v1/665cc1ddc9d1a2f00469275f.png"},{"id":105566322,"identity":"cbd90683-bab6-451e-9318-650757db292d","added_by":"auto","created_at":"2026-03-27 12:56:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":377100,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves comparing Actigraph-derived thresholds of ≥30 and ≥45 minutes of moderate-to-vigorous physical activity (MVPA) per day against YPAQ-reported MVPA minutes. The area under the curve (AUC) was 0.68 (95% CI: 0.62-0.74) for ≥30 minutes/day and 0.64 (95% CI: 0.58-0.71) for ≥45 minutes/day of MVPA.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8847825/v1/51181eaceeeb2eb3f1b93fcd.png"},{"id":105569708,"identity":"380a7aca-ec3f-4085-b7aa-755bdc988d17","added_by":"auto","created_at":"2026-03-27 13:13:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1622376,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8847825/v1/1a49c9b4-08d0-4cb0-bafc-18cc939990a2.pdf"},{"id":105566257,"identity":"4f20d7c5-4d70-4116-9609-469f399d49f1","added_by":"auto","created_at":"2026-03-27 12:55:55","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":351198,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e. Flow diagram showing recruitment process for the study participants.\u003c/p\u003e","description":"","filename":"Supplementaryfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8847825/v1/3fc9e9df8d20ba26983a07cc.tif"},{"id":105410609,"identity":"e9c0e1b9-4f1f-4cb3-8415-49346bfdcc78","added_by":"auto","created_at":"2026-03-25 17:17:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8847825/v1/860badaedbd8f29c3449db07.docx"},{"id":105410606,"identity":"b51ca981-7019-4df9-a5bc-b27d0e7bd1b9","added_by":"auto","created_at":"2026-03-25 17:17:43","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17250,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8847825/v1/84fb8eb5a8b62074c71b71d7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validity and reliability of Youth Physical Activity Questionnaire (YPAQ) among Preadolescents and Adolescents in Low-Resource Communities in Karachi, Pakistan","fulltext":[{"header":"Background","content":"\u003cp\u003ePhysical activity (PA) during childhood and adolescence is a key determinant of healthy growth and development and plays a critical role in promoting favorable long-term health outcomes. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Active behaviors established early in life are more likely to persist in adulthood. According to the Global Burden of Disease Study, insufficient PA has contributed to rising rates of chronic diseases, such as type 2 diabetes, over the past three decades. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Sedentary behavior significantly contributes to energy imbalance, thereby increasing the risk of being overweight and obesity among children and adolescents. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Childhood obesity often persists into adulthood and has emerged as a pressing global public health concern. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Trend analysis suggested that childhood overweight and obesity rates are expected to reach 30% globally by 2030. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) This trend is alarming given its strong link with the persistent rise of non-communicable diseases (NCDs), particularly among children in South Asia. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eMeasuring PA is complex due to its multidimensional nature, particularly among children and adolescents. Objective tools such as accelerometers provide accurate PA assessments; however, their high cost and logistical demands limit their routine use in large-scale population based studies, especially in resource-constrained settings. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) As a result, PA estimation in children and youth relies on subjective instruments. Standardized tools such as the Youth Physical Activity Questionnaire (YPAQ) and its short/modified variants were originally developed by UK researchers and used in major European youth studies (e.g. ALSPAC) (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), but they have not been validated in South Asian children and adolescents. Accurate assessment of PA, particularly MVPA, among children is crucial for quantifying its frequency, duration, and intensity. Such measurements are vital for monitoring population trends, elucidating dose-response relationships, and evaluating the effectiveness of interventions designed to mitigate long-term risks of chronic diseases. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) The primary objectives of this study were to assess the criterion validity of the modified YPAQ against accelerometer data, considered as the gold standard for measuring MVPA, and to evaluate its test-retest reliability among school-aged preadolescents and adolescents in low-resource communities in Karachi, Pakistan. The secondary objectives included identifying optimal YPAQ thresholds for classifying MVPA using Actigraph data and exploring gender differences in activity patterns within these communities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis cross-sectional validation study was conducted between August 2011 to April 2012 in 10 randomly selected low- to middle-income urban communities of Karachi. The study utilized a Wellcome Trust-funded project platform aimed to evaluate the effectiveness of community-based hypertension control interventions. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Ethical approval was obtained from the Ethical Review Committee of Aga Khan University (AKU), Pakistan.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample size and sampling strategy:\u003c/h2\u003e \u003cp\u003eThe sampling strategy has been described in detail elsewhere. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). For the present study, all eligible children from the target households of the parent study were enumerated. From each household, one child was randomly selected, with the probability of selection weighted by household size to ensure representativeness across clusters. Sample size estimation was performed using PASS software (Version 2013; NCSS, Kaysville, UT, USA). A minimum sample size of 183 children was required to achieve 80% statistical power at a 5% significance level. This estimation was based on an anticipated effect size of \u0026plusmn;\u0026thinsp;0.20 between the null and alternative hypotheses, assuming alternative correlation (r) and intra-class correlation coefficient (ICC) values ranging from 0.30 to 0.70 for validity and reliability, respectively. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) A final target sample size of 202 participants was established to compensate for an expected 10% non-response rate and data loss stemming from insufficient Actigraph wear time recording.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubjective assessment of PA\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003e​\u003c/b\u003eSubjective assessment of PA was conducted using a modified version of YPAQ. This self-administered tool was originally developed by the Medical Research Council, Epidemiology Unit at the University of Cambridge, which has been validated for English adolescents and preadolescents. The YPAQ is based on the Children\u0026rsquo;s Leisure Activities Study Survey (CLASS), that records the frequency, duration, intensity, and type of PA over the past seven days. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) It includes forty-seven activities categorized into seven broader groups: travel to school, classroom, and recess activities; household chores; leisure time; sports; sedentary behavior; and non-school-related travel, with separate recalls for weekdays and weekends.\u003c/p\u003e\n\u003ch3\u003eModification and translation of the YPAQ\u003c/h3\u003e\n\u003cp\u003eTwo bilingual experts translated the YPAQ into Urdu, and back-translation into English to ensure accuracy and conceptual equivalence. A third expert reviewed the translations to resolve discrepancies and mediated the process to ensure linguistic accuracy, conceptual clarity, and cultural relevance. Content validity was confirmed by two independent subject experts specializing in sports science and physical education. The YPAQ was culturally adapted by replacing activities and games with those common among Asian children, such as Kabaddi-Kabaddi (a vigorous tag-and-wrestling game), Pahel-dooj (a festive hopping game), Kho-kho (a fast-paced team chase game), and Pithu-garam (a dodgeball-like game involving stacked stone targets). These activities were replaced with alternatives such as rugby, skiing, snowboarding, sledging, skateboarding, and dog walking, which have similar metabolic equivalent task (MET) values to wrestling, hopscotch, and dodgeball. Household chores were disaggregated into sweeping, mopping, cooking, dishwashing, and grocery shopping to enhance data accuracy, as recommended by subject experts. Feasibility and face validity was examined by piloting the final draft on 10% of the calculated sample in a comparable setting. Based on pilot feedback, minor revisions were made to enhance linguistic clarity and ensure cultural appropriateness, while preserving the original intent of each item. The YPAQ was administered twice to capture last-week recalls: first at the time of Actigraph deployment, and again upon device retrieval following the seven-day monitoring protocol.\u003c/p\u003e\n\u003ch3\u003eObjective Assessment of PA:\u003c/h3\u003e\n\u003cp\u003eThe objective assessment of PA was conducted via a triaxial accelerometer, the Actigraph GT3X (ActiGraph Corp., Pensacola, Florida, USA), a small (4.6 \u0026times; 3.3 \u0026times; 1.5 cm), lightweight (19 g), self-calibrated device that records motion data across three (vertical, medio-lateral, and antero-posterior) axes. The ActiGraph GT3X is a widely used instrument for measuring PA in school-aged children (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and provides accurate estimates of energy expenditure when compared with indirect calorimetry. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\n\u003ch3\u003eData collection procedure:\u003c/h3\u003e\n\u003cp\u003eSubjective PA was assessed using YPAQ, while objective measurements were collected with Actigraph GT3X accelerometers, worn continuously for seven days. Devices were distributed at the start and collected at the end of the monitoring period. Written informed consent was obtained from parents or guardians, and assent from children before participation. Baseline assessments included demographics, anthropometry, and blood pressure using standard protocols. Weight was measured in light clothing with a Solar Powered Digital Scale, model 1631 (Tanita Corp., Inc., Illinois, USA) and height without shoes using a portable stadiometer. Body mass index (BMI) was calculated as weight (kg) divided by height in (m) squared. Waist circumference was measured with inelastic tape at the midpoint between the iliac crest and the lower rib margin after exhalation. Overweight or obesity and central obesity were defined as age and sex-specific \u0026ge;\u0026thinsp;85th percentile of BMI and waist circumference. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Blood pressure (mmHg) was measured thrice in the sitting position using a digital M6 Omron monitor (Healthcare Europe B.V., Hoofddorp, Netherlands) and pediatric cuff after 5 minutes of rest. The average of the last two of three readings was included in the analysis. Hypertension was defined as systolic or diastolic BP \u0026gt;\u0026thinsp;95th percentile for age, sex, and height. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Children who had elevated BP at the initial visit were reassessed within two weeks to confirm their hypertension status. A demonstration of Actigraph use was provided for children in the presence of parents or proxies, (Please refer to Supplementary Fig.\u0026nbsp;1 for the study flow and data collection process). The epoch length of Actigraph was set to 60 seconds, a commonly used interval in field-based studies that permits extended data collection. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) The monitor was secured with a stretchable belt at the right midclavicular line on the waist and positioned near the body\u0026rsquo;s center of mass to ensure accurate recording of whole-body movements. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Parents and proxies were instructed to remove the monitor during water activities (swimming, showering, or bathing) and sleep. Daily reminders were provided by phone calls or text messages, and adherence was tracked with a daily log. Parents and children were informed about their obesity and hypertension status to facilitate further diagnosis and management, while Actigraph outputs were shared to communicate PA levels and highlight potential future health risks associated with insufficient activity.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Variables\u003c/h2\u003e \u003cp\u003eThe primary outcome variable for assessing criterion validity was defined as the correlation between MVPA time recorded by the Actigraph and the corresponding self-reported time minutes per week from YPAQ over seven consecutive days. Reliability of YPAQ was assessed using ICCs with 95% CIs, based on MVPA time (minutes per week) measured one week apart. Secondary outcomes included the optimal YPAQ-reported MVPA time (minutes per day) that corresponded to Actigraph-derived thresholds of 30\u0026ndash;45 minutes per day. Differences in coefficients, expressed as correlation coefficients (r) and ICCs, were compared across all intensity levels by age, sex, weight status using both Actigraph and YPAQ data collected over the same one-week period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData analysis:\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed via Stata/SE version 16.1 for Windows (StataCorp LP, College Station, TX, USA). Actigraph data was processed and analyzed on ActiLife software version 6.2.1 (ActiGraph Corp., Pensacola, FL, USA). Measurements were considered valid if the Actigraph was worn for at least 8 hours per day for a minimum of 4 days, including one weekend day. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) Periods of ten or more consecutive minutes of zero counts during waking hours were classified as non-wear time and excluded. Time spent in water-based activities, as reported on the YPAQ, was also excluded for criterion analysis. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) The average time spent in each activity domain was calculated by dividing the total time at each intensity by the number of valid days. Activity intensities were categorized using established counts per minute (cpm) thresholds for children and youth: less than one hundred for sedentary, less than 2,295 for light, less than 4,012 for moderate, and 4,012 or more for vigorous. Each YPAQ activity was assigned a youth-specific MET value and categorized as sedentary (\u0026lt;\u0026thinsp;1.5), light (\u0026lt;\u0026thinsp;3), moderate (\u0026lt;\u0026thinsp;6), or vigorous (\u0026ge;\u0026thinsp;6). The domain-specific duration for each intensity was calculated as the total reported minutes divided by seven.\u003c/p\u003e \u003cp\u003eNormality of continuous data was assessed before analysis. Data are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) for normally distributed variables, or as medians with interquartile ranges (IQRs) for skewed data. Group differences were assessed using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or Mann-Whitney U test for continuous variables and chi-square or Fisher\u0026rsquo;s exact test for categorical variables. Pearson\u0026rsquo;s correlation coefficient (r) and corresponding 95% confidence intervals (CIs) were computed to assess criterion validity. Cronbach\u0026rsquo;s alpha and the intra-class correlation coefficient (ICC) with 95% CIs were computed to evaluate the consistency and reliability of repeated YPAQ measures. Within- and between-group coefficients were compared with the hypothesized benchmark values (r\u0026thinsp;=\u0026thinsp;0.30 for validity and ICC\u0026thinsp;=\u0026thinsp;0.70 for reliability) using Fisher\u0026rsquo;s z-transformation. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) The accuracy of the YPAQ was evaluated using nonparametric ROC curves and corresponding AUCs, based on ActiGraph thresholds of 30 and 45 minutes per day of MVPA. Statistical significance was set at a two-tailed p-value less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e Of the 285 invited 9\u0026ndash;14 years\u0026rsquo; children, 252 (88.4%) consented, and 234 (93.0%) provided valid accelerometer data. The mean age of participants was 11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 years. Males accounted for 51.6% of the sample, reflecting an equal sex distribution (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Proxy respondents assisted 97% of children during the assessment. The prevalence of hypertension was 17.0% (95% CI: 12.4\u0026ndash;21.6%), overweight or obesity 15.0% (95% CI: 10.6\u0026ndash;19.4%), and central obesity 22.0% (95% CI: 16.9\u0026ndash;27.1%). Girls exhibited significantly higher adiposity markers than boys, including BMI (p\u0026thinsp;=\u0026thinsp;0.001), waist circumference (p\u0026thinsp;=\u0026thinsp;0.007), and mid-arm circumference (p\u0026thinsp;=\u0026thinsp;0.002). Most schools attended by participants (66%) did not provide planned, mandatory physical education sessions. Among those enrolled in schools with scheduled sessions, only 34% had access to at least one session per week.\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\u003eComparision of sociodemographic and clinical factors of study participants by gender, (n\u0026thinsp;=\u0026thinsp;252).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;252\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;130\u003c/p\u003e \u003cp\u003e(51.6%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;122\u003c/p\u003e \u003cp\u003e(48.4%)\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\u003eAge group (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;11 n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;14 n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76 (58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68 (55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.7 (\u0026plusmn;\u0026thinsp;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.6 (\u0026plusmn;\u0026thinsp;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.7 (\u0026plusmn;\u0026thinsp;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of education (grades)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5th n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77 (63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5th n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003eβ\u003c/sup\u003e\u003cb\u003ePES at school\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt least one per week n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnthropometrics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.2 (\u0026plusmn;\u0026thinsp;9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.9 (\u0026plusmn;\u0026thinsp;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.7 (\u0026plusmn;\u0026thinsp;9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142.8 (\u0026plusmn;\u0026thinsp;11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142.6 (\u0026plusmn;\u0026thinsp;12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143.1 (\u0026plusmn;\u0026thinsp;10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u0026pound;\u003c/sup\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.0 (\u0026plusmn;\u0026thinsp;3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.5 (\u0026plusmn;\u0026thinsp;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.6 (\u0026plusmn;\u0026thinsp;3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.0 (\u0026plusmn;\u0026thinsp;8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.6 (\u0026plusmn;\u0026thinsp;7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.5 (\u0026plusmn;\u0026thinsp;9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMid-arm circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.3 (\u0026plusmn;\u0026thinsp;3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.7 (\u0026plusmn;\u0026thinsp;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.9 (\u0026plusmn;\u0026thinsp;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight or obesity n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u0026pound;\u003c/sup\u003eCentral obesity n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Pressure status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114.1 (\u0026plusmn;\u0026thinsp;11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114.9 (\u0026plusmn;\u0026thinsp;11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.2 (\u0026plusmn;\u0026thinsp;10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.5 (\u0026plusmn;\u0026thinsp;8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.8 (\u0026plusmn;\u0026thinsp;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.3 (\u0026plusmn;\u0026thinsp;8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u0026yen;\u003c/sup\u003eHypertension n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e$\u003c/sup\u003eValid days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.7 (\u0026plusmn;\u0026thinsp;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.6 (\u0026plusmn;\u0026thinsp;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.8 (\u0026plusmn;\u0026thinsp;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActigraph recorded time (hr./week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYPAQ reported time (hr./week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.4\u0026thinsp;\u0026plusmn;\u0026thinsp;31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.2\u0026thinsp;\u0026plusmn;\u0026thinsp;34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.3\u0026thinsp;\u0026plusmn;\u0026thinsp;27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e*\u003c/sup\u003eThe \u003cem\u003ep-\u003c/em\u003evalues relate to gender differences derived from Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test for and Chi-squared test where appropriate. \u003csup\u003e$\u003c/sup\u003eMean (\u0026plusmn;\u0026thinsp;SD); \u003csup\u003eβ\u003c/sup\u003ePhysical Education Sessions. \u003csup\u003e\u0026pound;\u003c/sup\u003eAge and gender specific \u0026ge;\u0026thinsp;85th percentile of BMI (Kg/m\u003csup\u003e2\u003c/sup\u003e) and waist circumference (cm). \u003csup\u003e\u0026yen;\u003c/sup\u003eHypertension defined as age, gender, and height specific \u0026gt;\u0026thinsp;95th percentile of SBP or DBP mmHg. \u003csup\u003e$\u003c/sup\u003eValid days: Actigraph data was considered valid if worn\u0026thinsp;\u0026ge;\u0026thinsp;8 hours/day for \u0026ge;\u0026thinsp;4 days, including at least one weekend day.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eChildren wore the accelerometer for an average of 6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 days, with comparable compliance between girls and boys (p\u0026thinsp;=\u0026thinsp;0.399). The total PA duration demonstrated consistency between the two methods (p\u0026thinsp;=\u0026thinsp;0.288) with the Actigraph mean recording hours of (76.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9) versus (79.4\u0026thinsp;\u0026plusmn;\u0026thinsp;31.7) reported hours on the YPAQ.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of MVPA between the YPAQ and accelerometer:\u003c/h2\u003e \u003cp\u003eThe mean duration of MVPA reported on the YPAQ (1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 hours; \u0026asymp;66 minutes) was significantly higher than that measured by the Actigraph (0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 hours; \u0026asymp;30 minutes; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Most children did not achieve the recommended guideline of \u0026ge;\u0026thinsp;60 minutes of MVPA per day. Only twenty-five children (10.7%) met this standard according to Actigraph data, compared with ninety-three children (39.7%) who self-reported meeting the desired benchmark on the YPAQ (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\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\u003ea. Descriptive Statistics Comparing total and moderate to vigrous physical activity between YPAQ and Actigraph measures, (n\u0026thinsp;=\u0026thinsp;234).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstrument\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYPAQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActigraph\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime assessed (hrs./week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.4\u0026thinsp;\u0026plusmn;\u0026thinsp;31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent in MVPA\u003csup\u003e$\u003c/sup\u003e (mean hrs./day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent in MVPA\u003csup\u003e$\u003c/sup\u003e (median min/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.8 (21.7\u0026ndash;93.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3 (12.7\u0026ndash;44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60 min of MVPA/day n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e*\u003c/sup\u003eThe \u003cem\u003ep-\u003c/em\u003evalues derived from Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test for and Chi-squared test as appropriate.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e$\u003c/sup\u003eMVPA= Moderate and vigorous physical activities.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb. Descriptive Statistics Comparing total and moderate to vigrous physical activity between YPAQ and Actigraph measures by gender, (n\u0026thinsp;=\u0026thinsp;234).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;122\u003c/p\u003e \u003cp\u003e(52.1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;112\u003c/p\u003e \u003cp\u003e(47.9%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTime spent in MVPA\u003csup\u003e$\u003c/sup\u003e hrs./day):\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYPAQ (median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.48\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57 (0.28-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActigraph (median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.40\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24 (0.13\u0026ndash;0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;60 min of MVPA/day\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYPAQ n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActigraph n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e*The p-values relate to gender differences derived from Student\u0026rsquo;s t-test and Chi-squared test where appropriate. \u003csup\u003e$\u003c/sup\u003eMVPA= Moderate and vigorous physical activities.\u003c/p\u003e \u003cp\u003eYPAQ METs for MVPA: 3 to \u0026lt;\u0026thinsp;6 for moderate and \u0026ge;\u0026thinsp;6 for vigorous activities; Actigraph counts/min for MVPA: 2295 to \u0026lt;\u0026thinsp;4012 for moderate and \u0026ge;\u0026thinsp;4012 for vigorous activities.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCriterion Coefficient for YPAQ-based MVPA relative to Actigraph Accelerometer:\u003c/h2\u003e \u003cp\u003eThe criterion correlation among children was [r\u0026thinsp;=\u0026thinsp;0.37; (95% CI: 0.29\u0026ndash;0.44)], indicating a modest positive association that met the hypothesized threshold rho ρ of \u0026gt;\u0026thinsp;0.30 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Agreement was modest for both weekdays [r\u0026thinsp;=\u0026thinsp;0.35; (95% CI: 0.27\u0026ndash;0.42)] and weekends [r\u0026thinsp;=\u0026thinsp;0.33; (95% CI: 0.25\u0026ndash;0.41)], with slightly higher correlations observed on weekdays, suggesting better agreement during structured days. In contrast, agreement for total weekly activities was very weak [r\u0026thinsp;=\u0026thinsp;0.13; (95% CI: 0.03\u0026ndash;0.23)], reflecting only a minimal positive relationship between the instruments (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea. Criterion correlation coefficients (95% CIs) between YPAQ and Actigraph measured activities by intensity, weekday, and weekend days in children, (n\u0026thinsp;=\u0026thinsp;234).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003cp\u003eIntensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003er (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeekday\u003c/p\u003e \u003cp\u003er (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeekend\u003c/p\u003e \u003cp\u003er (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.29\u0026ndash;0.44)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35 (0.27\u0026ndash;0.42)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33 (0.25\u0026ndash;0.41)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedentary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12 (0.03\u0026ndash;0.22)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11 (0.01\u0026ndash;0.20)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04 (-0.14-0.06)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 (-0.07-0.13)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02 (-0.08-0.12)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07 (-0.03-0.17)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13 (0.03\u0026ndash;0.23)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13 (0.03\u0026ndash;0.22)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05 (-0.05-0.15)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eYPAQ reported activity intensities thresholds METs: \u0026lt;1.5 for sedentary, \u0026lt;\u0026thinsp;3 for light, \u0026lt;\u0026thinsp;6 for moderate and \u0026ge;\u0026thinsp;6 for vigorous activities. Actigraph activity intensities thresholds counts/min: \u0026lt;100 for sedentary, \u0026lt;\u0026thinsp;2295 for light, \u0026lt;\u0026thinsp;4012 for moderate and \u0026ge;\u0026thinsp;4012 for vigorous activities; \u003csup\u003e$\u003c/sup\u003eMVPA= Moderate and vigorous physical activities.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe p-values for within- and between-group differences were calculated against the hypothesized r\u0026thinsp;\u0026ge;\u0026thinsp;0.30 using Fisher\u0026rsquo;s z-transformation. Single asterisk (*) indicates coefficients significantly above the reference value; double asterisk (**) indicates coefficients significantly below the reference value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb: Correlation coefficients (95% CIs) between YPAQ and Actigraph measured activities by gender, (n\u0026thinsp;=\u0026thinsp;234)\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003cp\u003eIntensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;123\u003c/p\u003e \u003cp\u003er (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;111\u003c/p\u003e \u003cp\u003er (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25 (0.13\u0026ndash;0.36)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28 (0.15\u0026ndash;0.40)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedentary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19 (0.07\u0026ndash;0.31)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07 (-0.06-0.19)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 (-0.08-0.17)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11 (-0.02-0.24)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18 (0.07\u0026ndash;0.29)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10 (-0.03-0.22)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eYPAQ reported activity intensities thresholds METs: \u0026lt;1.5 for sedentary, \u0026lt;\u0026thinsp;3 for light, \u0026lt;\u0026thinsp;6 for moderate and \u0026ge;\u0026thinsp;6 for vigorous activities. Actigraph activity intensities thresholds counts/min: \u0026lt;100 for sedentary, \u0026lt;\u0026thinsp;2295 for light, \u0026lt;\u0026thinsp;4012 for moderate and \u0026ge;\u0026thinsp;4012 for vigorous activities; \u003csup\u003e$\u003c/sup\u003eMVPA= Moderate and vigorous physical activities.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eThe p-values for within- and between-group differences were calculated against the hypothesized r\u0026thinsp;\u0026ge;\u0026thinsp;0.30 using Fisher\u0026rsquo;s z-transformation. Single asterisk (*) indicates coefficients significantly above the reference value; double asterisk (**) indicates coefficients significantly below the reference value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGender Differences in PA Levels Based on the Actigraph:\u003c/h2\u003e \u003cp\u003eBoys consistently reported higher MVPA levels than girls on both the YPAQ and Actigraph measures. Median MVPA duration by YPAQ was 1.09 hours/day (IQR: 0.48\u0026ndash;2.07) for boys and 0.57 hours/day (IQR: 0.28-1.00) for girls. The median MVPA by Actigraph, was 0.68 hours/day (IQR: 0.40\u0026ndash;0.95) for boys compared and only 0.24 hours/day (IQR: 0.13\u0026ndash;0.36) for girls. These differences were statistically significant across both instruments, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe proportion of children achieving\u0026thinsp;\u0026ge;\u0026thinsp;60 minutes of MVPA per day differed significantly by sex. According to YPAQ, 65 boys (53.3%) and 28 girls (25.0%) met the recommended threshold, whereas Actigraph data indicated that 23 boys (18.9%) and only 2 girls (1.8%) achieved the desired benchmark. Both differences were statistically significant, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eGender-stratified criterion analyses revealed modest and comparable coefficients for MVPA among boys [r\u0026thinsp;=\u0026thinsp;0.25; 9(5% CI: 0.13\u0026ndash;0.36)] and girls [r\u0026thinsp;=\u0026thinsp;0.28; (95% CI: 0.15\u0026ndash;0.40)], with no significant difference (p\u0026thinsp;=\u0026thinsp;0.654). Both estimates fell below the hypothesized benchmark, although girls demonstrated slightly higher coefficients (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). In addition, girls spent more time in sedentary activities and less time in light and MVPA compared with boys across all PA intensities p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAge-stratified analyses indicated modest agreement for adolescents aged 12\u0026ndash;14 years [r\u0026thinsp;=\u0026thinsp;0.40; (95% CI: 0.28\u0026ndash;0.51)] and preadolescents aged 9\u0026ndash;11 years [r\u0026thinsp;=\u0026thinsp;0.32; (95% CI: 0.16\u0026ndash;0.46)]. Agreement was slightly higher among normal-weight children [r\u0026thinsp;=\u0026thinsp;0.39; (95% CI: 0.31\u0026ndash;0.51)] compared with overweight or obese children [r\u0026thinsp;=\u0026thinsp;0.26; (95% CI: -0.08-0.56)], though these differences were not statistically significant (p values were 0.342 and 0.430 respectively) for both groups (Please see, Supplementary Table\u0026nbsp;1). The areas under the ROC curves indicated fair discrimination, with an AUC of 0.68 (95% CI: 0.62\u0026ndash;0.74) for the 30‑minute threshold and 0.64 (95% CI: 0.58\u0026ndash;0.71) for the 45‑minute threshold. Actigraph-derived MVPA thresholds of 30 minutes per day demonstrated 75.3% sensitivity for identifying active children and 56.9% specificity for detecting inactive children based on YPAQ responses. A trade off was evident, at the 45-minute threshold, sensitivity decreased to 59.7% while specificity increased to 68.4% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The YPAQ overestimated MVPA by approximately 40% consistently showing a ratio of 1.4 compared to objective measures across both applied thresholds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eReliability of the YPAQ:\u003c/h2\u003e \u003cp\u003eThe YPAQ demonstrated good internal consistency (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.75). Test-retest reliability for MVPA in children was strong, with an ICC of [0.73; (95% CI: 0.68\u0026ndash;0.77)], exceeding the 0.70 threshold and indicating stable, reproducible measurement (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea. Intra-class correlation coefficients (ICC) and 95% CIs of YPAQ repeat measures by intensity, week, and weekend day in children, (n\u0026thinsp;=\u0026thinsp;252)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003cp\u003eIntensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeekday\u003c/p\u003e \u003cp\u003eICC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeekend\u003c/p\u003e \u003cp\u003eICC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eICC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.61\u0026ndash;0.72)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.61\u0026ndash;0.72)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 (0.68\u0026ndash;0.77)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedentary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75 (0.70\u0026ndash;0.79)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61 (0.55\u0026ndash;0.66)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78 (0.73\u0026ndash;0.82)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.57\u0026ndash;0.67)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.60\u0026ndash;0.70)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69 (0.64\u0026ndash;0.74)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50 (0.44\u0026ndash;0.55)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58 (0.53\u0026ndash;0.63)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62 (0.57\u0026ndash;0.67)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eYPAQ reported activity intensities thresholds METs: \u0026lt;1.5 for sedentary, \u0026lt;\u0026thinsp;3 for light, \u0026lt;\u0026thinsp;6 for moderate and \u0026ge;\u0026thinsp;6 for vigorous activities. \u003csup\u003e$\u003c/sup\u003eMVPA= Moderate and vigorous physical activities.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe p-values for within- and between-group differences were calculated against the hypothesized ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.70 using Fisher\u0026rsquo;s z-transformation. Single asterisk (*) indicates coefficients significantly above the reference value; double asterisk (**) indicates coefficients significantly below the reference value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb. Intra-class correlation coefficients (ICC) and 95% CIs of YPAQ repeat measures by intensity and gender (n\u0026thinsp;=\u0026thinsp;252)\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003cp\u003eIntensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoys, n\u0026thinsp;=\u0026thinsp;130\u003c/p\u003e \u003cp\u003eICC (95% CIs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGirls, n\u0026thinsp;=\u0026thinsp;122\u003c/p\u003e \u003cp\u003eICC (95% CIs)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.59\u0026ndash;0.77)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.62\u0026ndash;0.78)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedentary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.71\u0026ndash;0.85)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.68\u0026ndash;0.82)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.63\u0026ndash;0.80)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66 (0.55\u0026ndash;0.74)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60 (0.47\u0026ndash;0.71)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63 (0.51\u0026ndash;0.72)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eYPAQ reported activity intensities thresholds METs: \u0026lt;1.5 for sedentary, \u0026lt;\u0026thinsp;3 for light, \u0026lt;\u0026thinsp;6 for moderate and \u0026ge;\u0026thinsp;6 for vigorous activities. \u003csup\u003e$\u003c/sup\u003eMVPA= Moderate and vigorous physical activities.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eThe p-values for within- and between-group differences were calculated against the hypothesized ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.70 using Fisher\u0026rsquo;s z-transformation. Single asterisk (*) indicates coefficients significantly above the reference value; double asterisk (**) indicates coefficients significantly below the reference value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eReliability was comparable for weekdays and weekends, [ICC\u0026thinsp;=\u0026thinsp;0.67; (95% CI: 0.61\u0026ndash;0.72)], slightly below the weekly benchmark. Reliability was acceptable both for boys [ICC\u0026thinsp;=\u0026thinsp;0.69; 95% CI: 0.59\u0026ndash;0.77)] and girls [ICC\u0026thinsp;=\u0026thinsp;0.71; (95% CI: 0.62\u0026ndash;0.78)], with no significant difference between groups p\u0026thinsp;=\u0026thinsp;0.678 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Reliability for total PA was also acceptable [ICC\u0026thinsp;=\u0026thinsp;0.62; (95% CI: 0.57\u0026ndash;0.67)], though below the 0.70 threshold.\u003c/p\u003e \u003cp\u003eStratified analyses indicated higher reliability among adolescents [ICC\u0026thinsp;=\u0026thinsp;0.79; (95% CI: 0.73\u0026ndash;0.84)] compared with younger children [ICC\u0026thinsp;=\u0026thinsp;0.66; 95% CI: 0.54\u0026ndash;0.76)]. Reliability was also higher among normal-weight children [ICC\u0026thinsp;=\u0026thinsp;0.74; (95% CI: 0.70\u0026ndash;0.78)] than overweight children [ICC\u0026thinsp;=\u0026thinsp;0.69; (95% CI: 0.51\u0026ndash;0.82)], suggesting more stable measurement in older and normal-weight groups (Please see, Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis community-based validation study of the modified YPAQ against the Actigraph accelerometer addresses an important gap in self-reported PA assessment and provides a foundation for developing culturally tailored, context-appropriate surveillance tools. The YPAQ demonstrated moderate agreement with Actigraph-derived MVPA estimates, with a correlation coefficient of r\u0026thinsp;=\u0026thinsp;0.37. These findings are consistent with previous studies conducted in high-income countries, which reported correlation coefficients ranging from 0.30 to 0.47 among similar age groups. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) To date, no validation study has examined the standardized YPAQ against accelerometer-based measures in LMICs. However, several locally developed or adapted physical activity questionnaires in LMICs have demonstrated modest validity and acceptable reliability compared to objective measures. For instance, an Indian study employing the Madras Diabetes Research Foundation PA Questionnaire for Children (MPAQ-C) reported a moderate correlation with accelerometer-derived MVPA (r\u0026thinsp;=\u0026thinsp;0.41) and moderately strong test-retest reliability (ICC\u0026thinsp;=\u0026thinsp;0.77) among participants aged 10\u0026ndash;17 years, including both children and older youth. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) Similarly, Tanaka et al. in Japan observed modest validity between the WHO Health Behavior in School-aged Children (HBSC) questionnaire and accelerometer-based MVPA (r\u0026thinsp;=\u0026thinsp;0.35) along with moderately strong reproducibility (ICC\u0026thinsp;=\u0026thinsp;0.72). (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) In China, Wang et al. reported comparable validity (r\u0026thinsp;=\u0026thinsp;0.36) and reliability (ICC\u0026thinsp;=\u0026thinsp;0.75) for the PAQ-C among school-aged children and adolescents. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis study extends previous research by validating the detailed Youth PA Questionnaire (YPAQ) in an LMIC context, demonstrating comparable performance to other self-report instruments for assessing MVPA against accelerometer-derived estimates among urban school-aged children and adolescents. The observed criterion coefficient (r\u0026thinsp;=\u0026thinsp;0.37) reflects a modest positive correlation, consistent with the expected benchmark of ρ\u0026thinsp;\u0026ge;\u0026thinsp;0.30. While modest, this level of agreement underscores the relevance of MVPA in the prevention of non-communicable diseases, particularly cardiovascular conditions. These findings emphasize the importance of public health promoting adequate PA among children to mitigate future NCD risk.\u003c/p\u003e \u003cp\u003eSelf-report PA assessment tools often overestimate MVPA intensity and duration due to recall errors and social desirability bias. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) Our ROC curve analysis, which is rarely employed in validation studies (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), confirmed this trend. The YPAQ overestimated MVPA by approximately 40% at both 30‑ and 45‑minute daily thresholds in our sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, self-reported measurements should be interpreted with caution, and appropriate calibrations or adjustments should be considered when accelerometer-based assessments are unavailable. In contrast, total activity measured by the YPAQ demonstrated only a weak association with accelerometer-based assessment. This finding is expected, as total activity encompasses lower-intensity and sporadic movements that are difficult for children to recall accurately and are inherently more complex. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eGender-stratified analyses revealed modest correlations for both boys and girls, indicating limited agreement between the instruments. Trivial differences may reflect variations in activity type, intensity, or recall accuracy. Girls often participate in less sporadic but more structured activities, which are easier to report. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) In our study, only 11% of children achieved the recommended MVPA. Girls consistently engaged in lower levels of MVPA than boys, as observed with both instruments. This pattern reflects global trends, with pooled data from 64 LMICs showing that boys are 1.6 times more likely than girls to meet recommended physical activity levels. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) Targeted, gender-specific, and culturally sensitive strategies are therefore essential, as girls may respond differently to standard interventions and face greater long-term health risks associated with sedentary lifestyles.\u003c/p\u003e \u003cp\u003eQuestionnaire-based methods are practical and cost-effective, although they remain subject to recall bias. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) Our age-stratified analysis indicated that preadolescents often struggle to distinguish between activity intensity levels, likely due to developmental and cognitive limitations, which may lead to misclassification. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) Among adolescents, both under- and over-reporting of MVPA are possible, as this group may be more influenced by social-desirability bias. Children aged 9\u0026ndash;11 years, who typically engage in light or intermittent activities, tend to underestimate or misclassify the intensity of past behaviors when recalling them. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) (Please see, Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eThe YPAQ demonstrated acceptable internal consistency (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.75) and reasonable test-retest reliability for MVPA (ICC\u0026thinsp;=\u0026thinsp;0.73), indicating consistent reporting among children. Reliability was higher among girls, adolescents, and normal-weight children, with ICC values within the good range. These findings suggest that the YPAQ is a reliable tool for estimating MVPA in older and normal-weight children. Self-report tools such as the YPAQ should therefore be used primarily to estimate MVPA when accelerometer data are unavailable. They are suitable for general assessments or screening purposes but lack the precision required for detailed measurement. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eOur study offers several strengths. Its community-based design enabled validation of the detailed YPAQ against Actigraph measures in a representative sample of school-aged children and adolescents from a low-resource urban setting. Gender stratified analysis found significant differences in MVPA patterns that consistent with global data thereby supporting the focus on targeted interventions in low- and middle-income countries. We applied intensity-specific cpm thresholds and 60-second epochs, as recommended by Evenson et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), to enhance classification accuracy and support regional standardization. Shorter epoch lengths (e.g., 5\u0026ndash;15 seconds) may further improve detection of brief MVPA bursts common in children. Newer Actigraph models, such as the GT9X Link, provide high-resolution data and are valuable for pediatric physical activity research. Beyond correlation estimates, we employed ROC curve analysis to assess estimation errors for MVPA between instruments (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), a methodological approach with important implications for public health and policy. To minimize potential information bias, the same proxy respondent assisted most of the children during data collection. Most participants complied with the study protocol by wearing the Actigraph for the required duration.\u003c/p\u003e \u003cp\u003eThese findings should be interpreted with caution due to several limitations. We operated under the assumption that participants\u0026rsquo; activity patterns would not significantly change over the course of the study. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) The exclusive recruitment of urban school participants limits the generalizability to rural and those out-of-school children. Potential device tampering could not be monitored, which is a common challenge in community-based research. An initial Hawthorne effect (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) may occur when the Actigraph is first worn, however, activity data from the first and last days suggest this effect was minimal. Water-based activities were excluded because the Actigraph was not worn during these periods; however, newer water-resistant models (e.g., GT3X\u0026thinsp;+\u0026thinsp;and GT9X Link) can now capture water related activities and offer more flexible data processing. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Few children achieved the recommended 60 minutes of MVPA per day, so we applied lower thresholds of 30 and 45 minutes for comparison with YPAQ results. This finding aligns with evidence that effective youth activity programs include 30\u0026ndash;45 minutes of continuous MVPA multiple times per week. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Our sampling strategy was based on cluster size, but the coefficients were not adjusted for clustering. The latest WHO guidelines recommend subgroup analyses to examine whether physical activity patterns and health outcomes differ by age, sex, body weight, race/ethnicity, and socioeconomic status. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) Future research should therefore include larger, more diverse samples to enable such analyses. Selecting appropriate accelerometer cutoff points remains a methodological challenge, as thresholds vary by age and sex. Using uniform thresholds risks misclassification, underscoring the need for population-specific calibration. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) Finally, the high cost of Actigraph devices (approximately USD 2,000\u0026ndash;3,000 per unit, including software) limits their feasibility in large-scale studies, particularly in low-resource settings. (6, 47) Evaluating affordable, validated wearable devices is therefore critical to support physical activity research in LMICs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study concludes that the YPAQ has modest validity and moderately strong reliability for assessing MVPA among school-going adolescents and preadolescents in low- to middle-income urban communities in Pakistan. Despite its utility for measuring MVPA the YPAQ exhibits limited correlation with total PA and should therefore not be used for a comprehensive assessment of overall PA. For greater accuracy, both MVPA and total PA should be measured with accelerometers, though high cost prevents large scale deployment of this gold standard. Future research should prioritize the development of culturally relevant and reliable tools to accurately capture PA patterns among South Asian children and youth a population with an elevated susceptibility to chronic diseases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC:\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u003c/strong\u003eArea Under the Curve\u003c/p\u003e\n\u003cp\u003eMETs:\u0026nbsp;Metabolic Equivalent Task Scores\u003c/p\u003e\n\u003cp\u003eMVPA: Moderate to Vigorous Physical Activity\u003c/p\u003e\n\u003cp\u003eROC: Receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003eYPAQ: Youth Physical Activity Questionnaire\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Ethical Review Committee (ERC) of Aga Khan University,\u0026nbsp;Pakistan (1643-CHS-ERC-10).\u0026nbsp;Written informed consent was obtained from the parents or guardians, and assent was obtained from the participating children.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consented to the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript have no potential, perceived, or real conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by the Wellcome Trust, UK, through the M.Sc. Fellowship grant # 090680/Z/09/Z awarded to Shiraz Hashmi under the supervision of Professor Tazeen H. Jafar. The design, conduct, analysis,\u0026nbsp;interpretation, and presentation\u0026nbsp;of the data was the responsibility of the authors, with no involvement from the funding agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSH conducted the study, coordinated the data collection, performed the preliminary analysis, drafted the initial manuscript, and carried out subsequent revisions. IQ and KA contributed to the study design, reviewed the analytic procedures, assisted in data analysis, data interpretation and critically reviewed the manuscript. TJ conceptualized and mentored the study, critically reviewed the analytic procedures and interpretations, and critically reviewed the manuscript. AA reviewed the final draft of the manuscript for important intellectual content. All the authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the translators and subject matter experts for their contributions in adapting and refining the YPAQ. We also appreciate the field and administrative staff for their support in data collection and study implementation. Most importantly, we are grateful to the children and their parents for their time and cooperation, which made this study possible.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eImplications and Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNovelty:\u0026nbsp;\u003c/strong\u003eThis\u0026nbsp;\u003cstrong\u003ecommunity-based study\u003c/strong\u003e is the first in the region to validate a standardized Youth Physical Activity Questionnaire (YPAQ) against objective accelerometer-based measures in school aged children and adolescents facilitating the development of culturally appropriate surveillance tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal Relevance\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eThe modified YPAQ demonstrated modest criterion validity for assessing moderate-to-vigorous physical activity (MVPA) consistent with findings from international studies, which enhances global knowledge base on physical activity assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCaution:\u0026nbsp;\u003c/strong\u003eConsistent withother self-report instruments, the YPAQ overestimates MVPA. Its use requires caution, and calibration or adjustment factors are recommended when objective devices (such as accelerometer) are unavailable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture steps:\u003c/strong\u003e Future efforts should prioritize the robust development of culturally validated physical activity instruments accounting for key demographic differences (age, sex, and body weight status) to design effective targeted interventions for population most vulnerable to inactivity.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStrong WB, Malina RM, Blimkie CJ, Daniels SR, Dishman RK, Gutin B, et al. Evidence based physical activity for school-age youth. J Pediatr. 2005;146(6):732-7.https://doi.org/10.1016/j.jpeds.2005.01.055\u003c/li\u003e\n\u003cli\u003eYang L, Xie D, Liu F, Lin J, Lin X, Chen Y, et al. Global and Regional Burden of Type 2 Diabetes Mellitus Attributable to Low Physical Activity From 1990 to 2021. J Diabetes. 2025;17(1):e70043.https://doi.org/10.1111/1753-0407.70043\u003c/li\u003e\n\u003cli\u003eJafar TH, Qadri Z, Islam M, Hatcher J, Bhutta ZA, Chaturvedi N. Rise in childhood obesity with persistently high rates of under-nutrition among urban school-aged Indo-Asian children. Arch Dis Child. 2008;93(5):373-8.https://doi.org/10.1136/adc.2007.125641\u003c/li\u003e\n\u003cli\u003eGeserick M, Vogel M, Gausche R, Lipek T, Spielau U, Keller E, et al. Acceleration of BMI in early childhood and risk of sustained obesity. N Engl J Med. 2018;379(14):1303-12.https://doi.org/10.1056/NEJMoa1803527\u003c/li\u003e\n\u003cli\u003eGao L, Peng W, Xue H, Wu Y, Zhou H, Jia P, et al. Spatial\u0026ndash;temporal trends in global childhood overweight and obesity from 1975 to 2030: a weight mean center and projection analysis of 191 countries. Global Health. 2023;19(1):53.https://doi.org/10.1186/s12992-023-00954-5\u003c/li\u003e\n\u003cli\u003eMisra A, Khurana L. The metabolic syndrome in South Asians: epidemiology, determinants, and prevention. Metab Syndr Relat Disord. 2009;7(6):497-514.https://doi.org/10.1186/s12992-023-00954-5\u003c/li\u003e\n\u003cli\u003eLee YS, Komar J, Chia MYH. Physical Activity Measurement Methodologies: A Systematic Review in the Association of South East Asian Nations (ASEAN). Sports (Basel). 2021;9(5).https://doi.org/10.3390/sports9050069\u003c/li\u003e\n\u003cli\u003eMcCrorie PRW, Perez A, Ellaway A. The validity of the Youth Physical Activity Questionnaire in 12-13-year-old Scottish adolescents. BMJ Open Sport Exerc Med. 2016;2(1):e000163.https://doi.org/10.1136/bmjsem-2016-000163\u003c/li\u003e\n\u003cli\u003eCorder K, van Sluijs EM, Wright A, Whincup P, Wareham NJ, Ekelund U. Is it possible to assess free-living physical activity and energy expenditure in young people by self-report? Am J Clin Nutr. 2009;89(3):862-70.https://doi.org/10.3945/ajcn.2008.26739\u003c/li\u003e\n\u003cli\u003eBrooke HL, Corder K, Griffin SJ, Ekelund U, van Sluijs EM. More of the same or a change of scenery: an observational study of variety and frequency of physical activity in British children. BMC Public Health. 2013;13:761.https://doi.org/10.1186/1471-2458-13-761\u003c/li\u003e\n\u003cli\u003eNelson DE, Moon RW, Holtzman D, Smith P, Siegel PZ. Patterns of health risk behaviors for chronic disease: a comparison between adolescent and adult American Indians living on or near reservations in Montana. Journal of adolescent health. 1997;21(1):25-32.https://doi.org/10.1016/s1054-139x(96)00274-1\u003c/li\u003e\n\u003cli\u003eJafar TH, Hatcher J, Poulter N, Islam M, Hashmi S, Qadri Z, et al. Community-based interventions to promote blood pressure control in a developing country: a cluster randomized trial. Ann Intern Med. 2009;151(9):593-601.https://doi.org/10.7326/0003-4819-151-9-200911030-00004\u003c/li\u003e\n\u003cli\u003eIslam M, Jafar TH, Wood AR, De Silva NM, Caulfield M, Chaturvedi N, et al. Multiple genetic variants explain measurable variance in type 2 diabetes-related traits in Pakistanis. Diabetologia. 2012;55(8):2193-204.https://doi.org/10.1007/s00125-012-2560-y\u003c/li\u003e\n\u003cli\u003eJafar TH, Qadri Z, Chaturvedi N. Coronary artery disease epidemic in Pakistan: more electrocardiographic evidence of ischaemia in women than in men. Heart. 2008;94(4):408-13.https://doi.org/10.1136/hrt.2007.120774\u003c/li\u003e\n\u003cli\u003eWalter SD, Eliasziw M, Donner A. Sample size and optimal designs for reliability studies. Stat Med. 1998;17(1):101-10.https://doi.org/10.1002/(sici)1097-0258(19980115)17:1\u0026lt;101::aid-sim727\u0026gt;3.0.co;2-e\u003c/li\u003e\n\u003cli\u003eTelford A, Salmon J, Jolley D, Crawford D. Reliability and validity of physical activity questionnaires for children: The Children\u0026rsquo;s Leisure Activities Study Survey (CLASS). Pediatric exercise science. 2004;16(1):64-78.https://doi.org/10.1123/pes.16.1.64\u003c/li\u003e\n\u003cli\u003eJurado-Castro JM, Gil-Campos M, Llorente-Cantarero FJ. Methods recently used for the assessment of physical activity in children and adolescents. Curr Opin Clin Nutr Metab Care. 2022;25(5):298-303.https://doi.org/10.1097/mco.0000000000000847\u003c/li\u003e\n\u003cli\u003eCrouter SE, Horton M, Bassett Jr DR. Validity of ActiGraph child-specific equations during various physical activities. Med Sci Sports Exerc. 2013;45(7):1403.https://doi.org/10.1249/MSS.0b013e318285f03b\u003c/li\u003e\n\u003cli\u003eAlmas A, Jafar TH. Adiposity and blood pressure in South Asian children and adolescents in Karachi. Am J Hypertens. 2011;24(8):876-80.https://doi.org/10.1038/ajh.2011.67\u003c/li\u003e\n\u003cli\u003eFalkner B, Daniels SR. Summary of the fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Hypertension. 2004;44(4):387-8.https://doi.org/10.1161/01.HYP.0000143545.54637.af\u003c/li\u003e\n\u003cli\u003eRiddoch CJ, Mattocks C, Deere K, Saunders J, Kirkby J, Tilling K, et al. Objective measurement of levels and patterns of physical activity. Arch Dis Child. 2007;92(11):963-9.https://doi.org/10.1136/adc.2006.112136\u003c/li\u003e\n\u003cli\u003eTrost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37(11 Suppl):S531-43.https://doi.org/10.1249/01.mss.0000185657.86065.98\u003c/li\u003e\n\u003cli\u003eTrost SG, Pate RR, Freedson PS, Sallis JF, Taylor WC. Using objective physical activity measures with youth: how many days of monitoring are needed? Med Sci Sports Exerc. 2000;32(2):426-31.https://doi.org/10.1097/00005768-200002000-00025. \u003c/li\u003e\n\u003cli\u003eRiddoch CJ, Bo Andersen L, Wedderkopp N, Harro M, Klasson-Heggebo L, Sardinha LB, et al. Physical activity levels and patterns of 9 -15 years-old European children. Med Sci Sports Exerc. 2004;36(1):86-92.https://doi.org/10.1249/01.MSS.0000106174.43932.92.\u003c/li\u003e\n\u003cli\u003eEvenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26(14):1557-65.https://doi.org/10.1080/02640410802334196\u003c/li\u003e\n\u003cli\u003eRidley K, Ainsworth BE, Olds TS. Development of a compendium of energy expenditures for youth. Int J Behav Nutr Phys Act. 2008;5:45.https://doi.org/10.1186/1479-5868-5-45\u003c/li\u003e\n\u003cli\u003eAinsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32(9 Suppl):S498-504.https://doi.org/10.1097/00005768-200009001-00009\u003c/li\u003e\n\u003cli\u003eFisher RA. On the\u0026quot; Probable Error\u0026quot; of a Coefficient of Correlation Deduced from a Small Sample. Metron. 1921;1:3-32\u003c/li\u003e\n\u003cli\u003eJanz KF, Lutuchy EM, Wenthe P, Levy SM. Measuring activity in children and adolescents using self-report: PAQ-C and PAQ-A. Med Sci Sports Exerc. 2008;40(4):767-72.https://doi.org/10.1249/MSS.0b013e3181620ed1\u003c/li\u003e\n\u003cli\u003eLachat CK, Verstraeten R, Khanh le NB, Hagstromer M, Khan NC, Van Ndo A, et al. Validity of two physical activity questionnaires (IPAQ and PAQA) for Vietnamese adolescents in rural and urban areas. Int J Behav Nutr Phys Act. 2008;5:37.https://doi.org/10.1186/1479-5868-5-37\u003c/li\u003e\n\u003cli\u003eLubans DR, Sylva K, Osborn Z. Convergent Validity and Test Retest Reliability of the Oxford Physical Activity Questionnaire for Secondary School Students. Behavior Change. 2008;25(01):23-34.https://doi.org/10.1375/bech.25.1.23\u003c/li\u003e\n\u003cli\u003eWang JJ, Baranowski T, Lau WP, Chen TA, Pitkethly AJ. Validation of the Physical Activity Questionnaire for Older Children (PAQ-C) among Chinese Children. Biomed Environ Sci. 2016;29(3):177-86.https://doi.org/10.3967/bes2016.022\u003c/li\u003e\n\u003cli\u003eMarasso D, Lupo C, Collura S, Rainoldi A, Brustio PR. Subjective versus objective measure of physical activity: a systematic review and meta-analysis of the convergent validity of the physical activity questionnaire for children (PAQ-C). Int J Environ Res Public Health. 2021;18(7):3413.https://doi.org/10.3390/ijerph18073413\u003c/li\u003e\n\u003cli\u003eMehreen TS, Ranjani H, Anitha C, Jagannathan N, Pratt M, Mohan V, et al. Reliability and Validity of a Physical Activity Questionnaire for Indian Children and Adolescents. Indian Pediatr. 2020;57(8):707-11.https://doi.org/10.1007/s13312-020-1912-9\u003c/li\u003e\n\u003cli\u003eTanaka C, Kyan A, Takakura M, Olds T, Schranz N, Tanaka S. Validation of the Physical Activity Questions in the World Health Organization Health Behavior in School-Aged Children Survey Using Accelerometer Data in Japanese Children and Adolescents. J Phys Act Health. 2021;18(2):151-6.https://doi.org/10.1123/jpah.2019-0671\u003c/li\u003e\n\u003cli\u003eEkelund U, Tomkinson G, Armstrong N. What proportion of youth are physically active? Measurement issues, levels and recent time trends. Br J Sports Med. 2011;45(11):859-65.https://doi.org/10.1136/bjsports-2011-090190\u003c/li\u003e\n\u003cli\u003eChinapaw MJ, Mokkink LB, van Poppel MN, van Mechelen W, Terwee CB. Physical activity questionnaires for youth: a systematic review of measurement properties. Sports Med. 2010;40(7):539-63.https://doi.org/0.2165/11530770-000000000-00000\u003c/li\u003e\n\u003cli\u003eMarasso D, Lupo C, Collura S, Rainoldi A, Brustio PR. Subjective versus Objective Measure of Physical Activity: A Systematic Review and Meta-Analysis of the Convergent Validity of the Physical Activity Questionnaire for Children (PAQ-C). Int J Environ Res Public Health. 2021;18(7).https://doi.org/10.3390/ijerph18073413\u003c/li\u003e\n\u003cli\u003eTelford RM, Telford RD, Olive LS, Cochrane T, Davey R. Why Are Girls Less Physically Active than Boys? Findings from the LOOK Longitudinal Study. PLoS One. 2016;11(3):e0150041.https://doi.org/10.1371/journal.pone.0150041\u003c/li\u003e\n\u003cli\u003eRicardo LIC, Wendt A, Costa CDS, Mielke GI, Brazo-Sayavera J, Khan A, et al. Gender inequalities in physical activity among adolescents from 64 Global South countries. J Sport Health Sci. 2022;11(4):509-20.https://doi.org/10.1016/j.jshs.2022.01.007\u003c/li\u003e\n\u003cli\u003eHelmerhorst HJ, Brage S, Warren J, Besson H, Ekelund U. A systematic review of reliability and objective criterion-related validity of physical activity questionnaires. Int J Behav Nutr Phys Act. 2012;9:103.https://doi.org/10.1186/1479-5868-9-103\u003c/li\u003e\n\u003cli\u003eNeilson HK, Robson PJ, Friedenreich CM, Csizmadi I. Estimating activity energy expenditure: how valid are physical activity questionnaires? Am J Clin Nutr. 2008;87(2):279-91.https://doi.org/10.1093/ajcn/87.2.279\u003c/li\u003e\n\u003cli\u003eJago R, Zakeri I, Baranowski T, Watson K. Decision boundaries and receiver operating characteristic curves: new methods for determining accelerometer cut points. J Sports Sci. 2007;25(8):937-44.https://doi.org/10.1080/02640410600908027\u003c/li\u003e\n\u003cli\u003eGoran MI. Measurement issues related to studies of childhood obesity: assessment of body composition, body fat distribution, physical activity, and food intake. Pediatrics. 1998;101(3 Pt 2):505-18.https://doi.org/10.1542/peds.101.S2.505\u003c/li\u003e\n\u003cli\u003eCorder K, Ekelund U, Steele RM, Wareham NJ, Brage S. Assessment of physical activity in youth. J Appl Physiol. 2008;105(3):977-87.https://doi.org/10.1152/japplphysiol.00094.2008\u003c/li\u003e\n\u003cli\u003eChaput JP, Willumsen J, Bull F, Chou R, Ekelund U, Firth J, et al. 2020 WHO guidelines on physical activity and sedentary behaviour for children and adolescents aged 5-17\u0026thinsp;years: summary of the evidence. Int J Behav Nutr Phys Act. 2020;17(1):141.https://doi.org/10.1186/s12966-020-01037-z\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Accelerometer, GT3X, preadolescents, adolescents, habitual physical activity, low-resource settings, community-based design, validity and reliability, LMICs.","lastPublishedDoi":"10.21203/rs.3.rs-8847825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8847825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e:\u003c/p\u003e \u003cp\u003ePhysical activity (PA) during childhood is essential for healthy growth and development. Although objective devices provide accurate PA estimates, but are costly and resource-intensive, leading to reliance on subjective instruments. Such tools have not been validated among South Asian children and youth. The primary objectives of this study were to evaluate the criterion validity of the modified Youth Physical Activity Questionnaire (YPAQ) against accelerometer data considered as the gold standard for measuring moderate-to-vigorous physical activity (MVPA) and to assess test-retest reliability. Secondary objectives included identifying optimal YPAQ thresholds for MVPA based on Actigraph-derived data and examining gender differences in MVPA patterns within these communities.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThis cross-sectional validation study was conducted among school-going healthy children aged 9 to 14 years recruited from low-resource settings of Karachi. Participants\u0026rsquo; physical or mental disabilities were excluded. Physical activity was objectively measured using Actigraph GT3X accelerometers worn for seven consecutive days. YPAQ was administered twice, one week apart. Criterion validity was evaluated using correlation coefficients (r), while test-retest reliability was assessed with intra-class correlation coefficients (ICCs) with corresponding 95% confidence intervals. Internal consistency was evaluated using Cronbach\u0026rsquo;s alpha. Receiver operating characteristic (ROC) curves were constructed to assess MVPA reported on the YPAQ in correspondence with Actigraph-derived measures.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eOf the 252 enrolled children, 234 (93%) provided valid accelerometer data. The criterion validity coefficient for MVPA was modest [r\u0026thinsp;=\u0026thinsp;0.37, (95% CI:0.29\u0026ndash;0.44)]. Test-retest reliability was moderately strong [ICC\u0026thinsp;=\u0026thinsp;0.73, (95% CI:0.68\u0026ndash;0.77)], and internal consistency was good (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.75). The YPAQ demonstrated acceptable sensitivity, specificity, and area under the ROC curve in classifying per day of MVPA minutes. Gender-stratified analyses revealed weak criterion validity both for boys [r\u0026thinsp;=\u0026thinsp;0.25, (95% CI:0.13\u0026ndash;0.36)] and girls [r\u0026thinsp;=\u0026thinsp;0.28, (95% CI:0.15\u0026ndash;0.40)], while reliability estimates were comparable across genders.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe modified YPAQ demonstrated modest validity and acceptable reliability in assessing MVPA among school-going children and youth in urban settings. These findings highlight the need for ongoing refinement and cultural adaptation of physical activity assessment tools to ensure accurate and contextually relevant estimates among South Asian children and youth, a population facing heightened risk of chronic diseases.\u003c/p\u003e","manuscriptTitle":"Validity and reliability of Youth Physical Activity Questionnaire (YPAQ) among Preadolescents and Adolescents in Low-Resource Communities in Karachi, Pakistan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 17:17:34","doi":"10.21203/rs.3.rs-8847825/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-25T07:21:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T12:14:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291956205198802575137004907574474389185","date":"2026-04-13T13:39:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T06:00:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62554616381448988861019035641436291733","date":"2026-03-24T02:38:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-23T09:01:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-20T05:36:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-20T05:35:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Health, Population and Nutrition","date":"2026-02-11T06:14:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab7d51fd-dd8a-4482-bdc8-60b039ad2e7d","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T18:38:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 17:17:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8847825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8847825","identity":"rs-8847825","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}