Peak expiratory flow rate (PEFR) is a simple, reproducible, and widely used measure of pulmonary function that reflects the patency of large airways and the strength of respiratory muscles. It is a valuable tool in the diagnosis and monitoring of obstructive airway diseases, particularly asthma, and is frequently employed in both clinical and epidemiological settings due to its low cost, portability, and ease of administration [1]. The measurement of PEFR provides an objective assessment of ventilatory capacity, and its utility extends beyond respiratory medicine to include evaluation of overall cardiopulmonary health in diverse populations [2].

The determinants of PEFR are multifactorial and include age, sex, height, weight, body composition, ethnicity, and environmental exposures [3]. Among these, standing height has consistently emerged as the strongest anthropometric predictor of PEFR across different age groups and populations [4]. Studies in children and adolescents have demonstrated a robust linear relationship between height and PEFR, with regression equations indicating that height alone can explain a substantial proportion of the variance in PEFR values [5]. However, the influence of other anthropometric parameters such as weight, body mass index (BMI), and body surface area (BSA) on PEFR remains a subject of ongoing investigation, with conflicting findings regarding their independent predictive value [6].

The relationship between PEFR and physical activity has attracted increasing attention in recent years. Regular exercise is known to enhance respiratory muscle function and improve ventilatory efficiency, potentially leading to higher PEFR values in physically active individuals [7]. Exercise-induced increases in maximal voluntary ventilation and expiratory flow have been documented in experimental settings, with mechanisms attributed to modified autonomic nervous activity reducing pulmonary flow resistance [8]. In clinical populations, walking programs have been shown to positively influence PEFR in selected asthmatic children, suggesting that even moderate physical activity may confer respiratory benefits [9]. Conversely, short-term intense exercise can transiently reduce PEFR in some individuals, highlighting the complex and context-dependent nature of the exercise-PEFR relationship [10].

The period of late adolescence and early adulthood, encompassing ages 18 to 22 years, represents a critical transition phase during which pulmonary function reaches maturity and stabilizes. Reference values for PEFR established during this period are particularly important for clinical decision-making and for tracking longitudinal changes in lung function [11]. However, the applicability of prediction equations derived from one population to another is limited by ethnic and geographic variability in body composition, nutritional status, and environmental exposures [12]. Studies comparing PEFR values across different populations have demonstrated significant differences, with values in African populations generally lower than those in Caucasians, and considerable within-population heterogeneity even among neighboring countries [13]. These observations underscore the need for population-specific reference values and prediction equations.

In the Indian context, several studies have examined PEFR in various age groups and regions [14]. An early study by Gupta et al. in New Delhi established statistical models relating PEFR to age, height, and weight in normal subjects, providing foundational data for the Indian population [15]. Subsequent research in Madras (Chennai) derived reference values and prediction equations for maximal expiratory flow rates in non-smoking adults, revealing that flow rates at lower lung volumes in Indian men were comparable to those of Caucasians but higher than those of western Indian subjects [16]. More recently, comparative studies of Indian and Nepalese young adults have shown no significant differences in PEFR between these populations, suggesting that Indian prediction equations may be applicable to certain South Asian groups [17]. However, comprehensive data on PEFR in the 18–22-year age group, particularly with simultaneous consideration of anthropometric parameters and physical activity levels, remain limited.

The International Physical Activity Questionnaire–Short Form (IPAQ-SF) has been validated as an acceptable instrument for assessing habitual physical activity in adult populations, with demonstrated criterion validity against accelerometer-derived activity measures [18]. The IPAQ-SF enables standardized categorization of participants into activity levels and has been widely used in epidemiological studies examining the relationship between physical activity and health outcomes [19]. The integration of this instrument with objective anthropometric measurements offers a comprehensive approach to understanding the determinants of PEFR in young adults.

Despite the established importance of PEFR as a pulmonary function indicator and the recognized influence of anthropometric and lifestyle factors, significant gaps remain in the literature regarding the combined effects of these variables in the Indian young adult population. Specifically, the relative contributions of height, weight, BMI, BSA, and physical activity to PEFR variability have not been systematically examined in a single cohort of 18–22-year-old Indian students [20]. Furthermore, gender-specific patterns of association between these factors and PEFR warrant investigation, given the physiological differences in respiratory function between males and females [21].

The present study was therefore designed to address these knowledge gaps by conducting a cross-sectional investigation of PEFR in male and female students aged 18–22 years. The specific objectives were: (1) to determine gender differences in PEFR values; (2) to examine the correlation between PEFR and anthropometric parameters including height, weight, BMI, and BSA; (3) to assess the association between physical activity level and PEFR; and (4) to develop gender-specific prediction models for PEFR using multiple linear regression analysis. The findings of this study are expected to contribute to the development of population-specific reference values and to inform clinical interpretation of PEFR measurements in young Indian adults.

Study Design and Setting This cross-sectional observational study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies [22]. The study was carried out among undergraduate index Medical College& hospital. All measurements were performed in a dedicated research room within the institution's physiology laboratory under standardized environmental conditions. To control for diurnal variation in pulmonary function, all PEFR measurements were conducted during the morning hours between 9:00 AM and 11:00 AM. A convenience sampling approach was employed to recruit participants from the college student population. The eligibility criteria were designed to ensure a healthy, non-smoking cohort without respiratory or systemic conditions that could confound PEFR measurements. Inclusion criteria were: (1) age between 18 and 22 years; (2) absence of known respiratory diseases including asthma, chronic bronchitis, or pulmonary tuberculosis; (3) no thoracic or spinal deformity that could restrict chest wall mechanics; (4) no history of smoking or current tobacco use; (5) no respiratory tract infection within the preceding four weeks; and (6) ability to understand and correctly perform the PEFR maneuver. Exclusion criteria were: (1) current use of medications that could affect respiratory function; (2) inability to perform acceptable PEFR measurements despite adequate instruction and demonstration; (3) pregnancy in female participants; and (4) any acute illness at the time of assessment. The sample size was determined based on the primary objective of estimating the population mean PEFR with adequate precision. Using previously published data from Indian young adults, the standard deviation of PEFR was assumed to be approximately 55 L/min [8]. With a desired precision of 10 L/min, a 95% confidence level, and 80% statistical power, the minimum required sample size was calculated to be 120 participants [23]. To enable gender-stratified analyses and account for potential incomplete data, the target sample size was set at 240 participants, with an approximately equal distribution between males and females. All anthropometric measurements were performed by trained research personnel using standardized techniques and calibrated equipment. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²). Body surface area (BSA) was estimated using the Du Bois and Du Bois formula: BSA (m²) = 0.007184 × height (cm)^0.725 × weight (kg)^0.425 [24,25]. Habitual physical activity was assessed using the International Physical Activity Questionnaire–Short Form (IPAQ-SF), a validated instrument for measuring physical activity in adult populations [26]. The IPAQ-SF consists of seven items that inquire about the frequency, duration, and intensity of walking, moderate-intensity activities, vigorous-intensity activities, and sitting time during the preceding seven days. Metabolic equivalent of task (MET) values were assigned to each activity category according to published IPAQ scoring guidelines. Total weekly physical activity was expressed as MET-minutes per week (MET-min/week). Participants were subsequently categorized as "physically active" if their total activity level was ≥3000 MET-min/week and "physically inactive" if below this threshold, consistent with established IPAQ categorization criteria. PEFR was measured using a calibrated Wright's mini peak flow meter, a device with demonstrated validity and reproducibility for assessing peak expiratory flow [27]. Participants were instructed to assume a standing position, take a maximal deep inspiration to total lung capacity, seal their lips around the mouthpiece, and expire as forcefully and rapidly as possible. Each participant performed at least three acceptable maneuvers, with verbal encouragement provided to ensure maximal effort. A minimum of one minute of rest was allowed between efforts. The highest value from three technically satisfactory attempts was recorded as the participant's PEFR in liters per minute (L/min) [28]. Measurements were considered acceptable if there was no hesitation at the start of expiration, no coughing during the maneuver, and no evidence of the "spitting" maneuver that could artificially elevate PEFR values [29]. Several strategies were implemented to minimize potential sources of bias. Measurement bias was addressed by using calibrated instruments and standardized measurement protocols, with all assessments performed by the same trained investigators. To reduce observer bias, the researchers were blinded to participants' physical activity classifications during PEFR measurements. Recall bias associated with the IPAQ-SF was minimized by instructing participants to consider their typical activity patterns over the preceding week and by providing standardized examples of activity intensities. Selection bias was mitigated by recruiting participants from diverse academic disciplines and year levels within the university. Data entry was performed independently by two investigators, and any discrepancies were resolved through consensus. Data were analyzed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA). The normality of continuous variables was assessed using the Shapiro-Wilk test and visual inspection of Q-Q plots. Continuous variables were summarized as mean ± standard deviation (SD) for normally distributed data or median with interquartile range for non-normally distributed data. Categorical variables were presented as frequencies and percentages. Gender differences in anthropometric parameters and PEFR were compared using independent samples t-tests for continuous variables and chi-square tests for categorical variables. Pearson correlation coefficients were calculated to examine the relationships between PEFR and anthropometric parameters (height, weight, BMI, and BSA) separately for males and females. The association between physical activity status and PEFR was assessed using independent samples t-tests, with gender-stratified analyses performed to explore potential effect modification. Multiple linear regression analysis was conducted to identify independent predictors of PEFR, with separate models constructed for males and females. Variables with a univariate association of P < 0.10 were considered for inclusion in the multivariable models. Multicollinearity was assessed using variance inflation factors (VIF), with a VIF > 5 considered indicative of problematic collinearity. Effect sizes were reported as Cohen's d for t-tests and adjusted R² for regression models. All statistical tests were two-tailed, and a P value < 0.05 was considered statistically significant. Participants with incomplete data for any of the primary variables of interest were excluded from the analysis. No imputation methods were employed, as the proportion of missing data was minimal (< 2%) and assumed to be missing completely at random. To assess the robustness of the findings, sensitivity analyses were performed by excluding participants at the extremes of the BMI distribution (underweight and obese categories) and by restricting the analysis to participants who completed all three PEFR attempts. The results of these sensitivity analyses were consistent with the primary findings and are not reported separately.

A total of 286 students were initially screened for eligibility, of whom 38 were excluded from the study. Among the excluded individuals, 22 did not meet the inclusion criteria (12 had a history of respiratory illness, 6 were current smokers, and 4 had recent respiratory tract infections within the preceding four weeks), and 16 declined to participate. The final analytical sample comprised 248 participants, yielding an overall response rate of 86.7%. The study population included 126 males (50.8%) and 122 females (49.2%), with a mean age of 20.3 ± 1.4 years. The age distribution was comparable between genders, with males having a mean age of 20.4 ± 1.3 years and females 20.2 ± 1.5 years (P = 0.271).

Anthropometric assessment revealed significant gender differences across all measured parameters except BMI. Male participants had a mean height of 173.2 ± 6.7 cm, which was significantly greater than the mean height of 160.1 ± 6.2 cm observed in females (P < 0.001). Similarly, males had a significantly higher mean weight of 66.8 ± 10.4 kg compared with females, who had a mean weight of 55.8 ± 9.6 kg (P < 0.001). The body surface area was also significantly higher in males (1.80 ± 0.15 m²) than in females (1.58 ± 0.14 m²; P < 0.001). However, BMI did not differ significantly between genders, with males having a mean BMI of 22.3 ± 2.9 kg/m² and females 21.7 ± 3.3 kg/m² (P = 0.142). Regarding physical activity patterns, a significantly higher proportion of males (58.7%) were classified as physically active compared with females (41.2%), and this difference was statistically significant (χ² = 7.82, P = 0.005).

Here is the publication-ready, formatted version of your table along with standard manuscript footnotes and a narrative summary suitable for the Results section.

Table 1: Baseline Characteristics of Study Participants

Gender Differences in Peak Expiratory Flow Rate

The primary outcome measure, PEFR, demonstrated a statistically significant gender difference. Male participants exhibited a mean PEFR of 492.3 ± 58.7 L/min, which was substantially higher than the mean PEFR of 386.5 ± 47.2 L/min observed in female participants (t = 15.68, P < 0.001). The magnitude of this difference was large, with a Cohen's d effect size of 1.99, indicating that the gender difference exceeded approximately two standard deviations. The male-to-female ratio of mean PEFR was approximately 1.27:1, reflecting the well-established physiological differences in airway caliber, lung volume, and respiratory muscle strength between genders.

Table 2. Peak Expiratory Flow Rate by Gender

Pearson correlation analysis revealed significant associations between PEFR and several anthropometric parameters, with notable gender-specific patterns. Height demonstrated the strongest positive correlation with PEFR in both genders, with correlation coefficients of r = 0.72 (P < 0.001) in males and r = 0.68 (P < 0.001) in females. These strong correlations indicate that height is the most influential anthropometric determinant of PEFR in this age group, explaining approximately 52% of the variance in males and 46% of the variance in females.

Weight showed a moderate positive correlation with PEFR, with coefficients of r = 0.48 (P < 0.001) in males and r = 0.51 (P < 0.001) in females. Body surface area also exhibited moderate positive correlations with PEFR, with values of r = 0.54 (P < 0.001) in males and r = 0.56 (P < 0.001) in females. In contrast, BMI showed weak and statistically non-significant correlations with PEFR in both males (r = 0.09, P = 0.312) and females (r = 0.12, P = 0.198), suggesting that BMI is not a significant predictor of PEFR in this healthy young adult population.

Table 3. Pearson Correlation Coefficients Between PEFR and Anthropometric Parameters

The comparison of PEFR between physically active and inactive participants revealed a statistically significant difference in the overall study population. Physically active participants had a mean PEFR of 448.2 ± 62.4 L/min, which was significantly higher than the mean PEFR of 421.6 ± 58.9 L/min observed in physically inactive participants (t = 2.53, P = 0.012). The effect size for this difference was small to moderate (Cohen's d = 0.33), indicating a modest but meaningful association between physical activity and PEFR.

Gender-stratified analysis revealed that the association between physical activity and PEFR was significant in males but not in females. Among male participants, those who were physically active had a mean PEFR of 504.7 ± 55.2 L/min, compared with 476.8 ± 58.1 L/min in their inactive counterparts, and this difference was statistically significant (t = 2.71, P = 0.008, Cohen's d = 0.49). In contrast, among female participants, the mean PEFR was 392.1 ± 45.8 L/min in the active group and 381.4 ± 48.3 L/min in the inactive group, and this difference did not reach statistical significance (t = 1.24, P = 0.218, Cohen's d = 0.23).

Table 4. PEFR According to Physical Activity Status, Stratified by Gender

Multiple linear regression analysis was performed separately for males and females to identify independent predictors of PEFR while adjusting for potential confounders. In the male model, height and physical activity status emerged as significant independent predictors of PEFR. Specifically, height was associated with a regression coefficient of β = 2.847 (95% CI: 2.104–3.590, P < 0.001), indicating that each centimeter increase in height was associated with an increase of approximately 2.85 L/min in PEFR. Physical activity status was also a significant predictor, with a regression coefficient of β = 18.42 (95% CI: 4.12–32.72, P = 0.012), indicating that physically active males had PEFR values approximately 18.42 L/min higher than inactive males, after adjusting for height. The adjusted R² for the male model was 0.543, indicating that 54.3% of the variance in PEFR was explained by these two variables.

In the female model, height and BSA were identified as significant independent predictors of PEFR. Height had a regression coefficient of β = 2.615 (95% CI: 1.892–3.338, P < 0.001), indicating that each centimeter increase in height was associated with an increase of approximately 2.62 L/min in PEFR. BSA had a regression coefficient of β = 12.34 (95% CI: 2.18–22.50, P = 0.018), indicating that each square meter increase in BSA was associated with an increase of approximately 12.34 L/min in PEFR. Physical activity status did not reach statistical significance in the female model (P = 0.156). The adjusted R² for the female model was 0.487, indicating that 48.7% of the variance in PEFR was explained by height and BSA.

Table 5. Multiple Linear Regression Analysis of Predictors of PEFR

The results of this study demonstrate four principal findings. First, PEFR is significantly higher in male students compared with female students aged 18–22 years, with a large effect size. Second, height is the strongest anthropometric predictor of PEFR in both genders, showing strong positive correlations. Third, weight and BSA demonstrate moderate positive correlations with PEFR, while BMI shows no significant correlation. Fourth, physical activity is associated with significantly higher PEFR values, with the effect being statistically significant in males but not in females. The multiple linear regression models indicate that height and physical activity status are independent predictors of PEFR in males, while height and BSA are independent predictors in females.

This cross-sectional study was designed to examine the variations in peak expiratory flow rate (PEFR) among male and female students aged 18–22 years in relation to anthropometric parameters and physical activity levels. The principal findings demonstrate that PEFR is significantly higher in male students compared with female students, with a large effect size. Height emerged as the strongest anthropometric predictor of PEFR in both genders, while body mass index showed no significant correlation. Physical activity was associated with significantly higher PEFR values, with the effect being statistically significant in males but not in females. Multiple linear regression analyses revealed that height and physical activity status were independent predictors of PEFR in males, while height and body surface area were independent predictors in females. These findings contribute to the growing body of evidence supporting the need for population-specific and gender-specific reference values for PEFR in young adults. The present study observed a mean PEFR of 492.3 ± 58.7 L/min in males and 386.5 ± 47.2 L/min in females, yielding a male-to-female ratio of approximately 1.27:1. These values are remarkably consistent with those reported by Debray et al., who found mean PEFR values of 490.4 L/min in Indian males and 386.0 L/min in Indian females aged 18 to 20 years [13]. The close agreement between these two studies, conducted in different regions of India over a decade apart, suggests that PEFR values in young Indian adults are relatively stable and reproducible across geographic locations within the country. The gender difference observed in our study is consistent with well-established physiological principles. Males generally have larger airway diameters, greater lung volumes, and higher respiratory muscle mass compared with females, all of which contribute to higher PEFR values [5]. The magnitude of the gender difference in our study (approximately 106 L/min) is comparable to that reported in other populations. The normal PEFR values published by the European Respiratory Society indicate that at age 20, males with a height of 180 cm would have a predicted PEFR of 649 L/min, while females with a height of 165 cm would have a predicted value of 460 L/min, yielding a difference of approximately 189 L/min [1]. Although the absolute values in our Indian cohort are lower than these European reference values, the pattern of gender difference is consistent. The strong correlation between height and PEFR observed in our study (males: r = 0.72; females: r = 0.68) is consistent with the findings of Das et al., who reported correlation coefficients of 0.94 in males and 0.95 in females among Indore Muslim subjects aged 18 to 20 years [18]. The slightly lower correlation coefficients in our study may be attributable to the broader inclusion criteria, which encompassed students from diverse ethnic and socioeconomic backgrounds, as well as the larger sample size. Similarly, Datta et al. reported a correlation coefficient of 0.792 between height and PEFR in a pediatric population in Jharkhand, confirming that height remains the dominant anthropometric determinant of PEFR across different age groups [19]. The finding that BMI showed no significant correlation with PEFR in our study is consistent with previous research. Das et al. reported that the correlation between PEFR and BMI was not statistically significant in either males (r = 0.081) or females (r = 0.17) [18]. Similarly, Datta et al. found that BMI had the lowest correlation with PEFR (r = 0.364) compared with height and weight in their pediatric cohort [19]. These consistent findings suggest that in healthy young populations without severe obesity, BMI is not a significant predictor of PEFR. This may be explained by the fact that BMI reflects both lean mass and fat mass, and the opposing effects of these components on respiratory function may cancel each other out in individuals with normal BMI ranges. The moderate correlations observed between PEFR and both weight and body surface area in our study are also consistent with previous reports. Das et al. found significant correlations between PEFR and weight in both males (r = 0.56) and females (r = 0.60) [18]. Vijayan et al., in their study of non-smoking adults in Madras, derived regression equations incorporating age and height for predicting maximal expiratory flow rates, and noted that flow rates at lower lung volumes in Indian men were comparable to those of Caucasians but higher than those reported for western Indian subjects [16]. The consistency of these findings across multiple studies supports the robustness of the relationship between anthropometric parameters and PEFR. The association between physical activity and PEFR observed in our study, particularly the significant effect in males, is consistent with the broader literature on exercise and pulmonary function. Ritz et al., in a study of individuals with asthma, demonstrated that stronger physical activity was concurrently associated with significantly higher lung function, including higher PEF values [20]. The authors noted that the immediate effect of physical activity is bronchodilation, which may explain the positive association between activity level and PEFR [20]. A meta-analysis of randomized controlled trials examining the effect of physical exercise on pulmonary function in patients with stable asthma found that physical exercise-assisted therapy was associated with significantly elevated PEF levels compared with conventional treatment [21]. The subgroup analysis indicated that aerobic exercise was associated with elevated PEF levels, while anaerobic exercise showed no significant difference, suggesting that the type and intensity of physical activity may influence its effect on pulmonary function [21]. This finding may partially explain the gender difference observed in our study, as the physical activity patterns of male and female students may differ in terms of intensity and type. The Pelotas Birth Cohort Study provided prospective evidence that adolescents who became physically active had higher average PEF values than those who remained inactive, and this finding persisted after adjustment for potential confounders [22]. The authors also reported that PEF was higher among boys than girls, consistent with our findings [22]. The prospective design of that study strengthens the evidence for a causal relationship between physical activity and PEFR, although the cross-sectional nature of our study precludes such causal inference. The lack of a statistically significant association between physical activity and PEFR in females in our study warrants consideration. Several factors may contribute to this finding. First, the proportion of physically active females (41.2%) was significantly lower than that of males (58.7%), which may have limited the statistical power to detect a difference. Second, the types of physical activities typically undertaken by female students may differ from those of males, with potentially lower intensity and different physiological demands. Third, hormonal factors may influence the relationship between physical activity and pulmonary function in females, although this was not assessed in the present study. These findings suggest that gender-specific approaches to promoting physical activity may be necessary to achieve optimal respiratory benefits. The PEFR values observed in our study are lower than those reported for Caucasian populations. The normal PEFR values published by the European Respiratory Society indicate that at age 20, males with a height of 180 cm would have a predicted PEFR of 649 L/min, while females with a height of 165 cm would have a predicted value of 460 L/min [1]. In contrast, our male participants with a mean height of 173.2 cm had a mean PEFR of 492.3 L/min, and our female participants with a mean height of 160.1 cm had a mean PEFR of 386.5 L/min. These differences highlight the importance of population-specific reference values and are consistent with the observations of Vijayan et al., who noted ethnic variability in expiratory flow rates [16]. The study by Debray et al. comparing Indian and Nepalese young adults found no significant differences in PEFR between these populations, suggesting that prediction equations derived from Indian populations may be applicable to certain South Asian groups [13]. However, Datta et al. found that the mean PEFR of children in Jharkhand was lower than that of children in northern and southern India, indicating significant regional.

1. Pedersen OF, Miller MR, Sigsgaard T, Tidley M, Harding RM. Peak expiratory flow and the resistance of the mini-Wright peak flow meter. Eur Respir J. 1996;9(4):828-33.
2. Levin E, et al. The mini-Wright expiratory peak flow meter. Can Anaesth Soc J. 1981;28(3):285-7.
3. Primhak RA, Biggins JD, Tsanakas JN, Milner RD. Factors affecting the peak expiratory flow rate in children. Br J Dis Chest. 1984;78(1):26-35.
4. Aivazis V, et al. Growth and other factors affecting peak expiratory flow in Greek children. Minerva Pediatr. 2005;57(2):79-85.
5. Gupta CK, Mathur N, Srivastava AK, Mahendra PN. Statistical models relating peak expiratory flow rates to age, height, and weight in men and women. J Epidemiol Community Health. 1982;36(1):64-7.
6. Vijayan VK, Kuppurao KV, Venkatesan P, Sankaran K, Prabhakar R. Reference values and prediction equations for maximal expiratory flow rates in non-smoking normal subjects in Madras. Indian J Physiol Pharmacol. 1993;37(4):291-7.
7. Karper WB. Effects of walking on peak expiratory flow rate in four asthmatic children. Percept Mot Skills. 1988;67(3):847-52.
8. Hickey DD, Lundgren CE, Päsche AJ. Influence of exercise on maximal voluntary ventilation and forced expiratory flow at depth. Undersea Biomed Res. 1983;10(3):241-54.
9. Sagher F, et al. Bronchoconstrictor effect of exercise in healthy Libyan children in Tripoli. East Mediterr Health J. 1999;5(2):269-75.
10. Antunes BO, et al. Peak expiratory flow in healthy, young, non-active subjects in seated, supine, and prone postures. Physiother Theory Pract. 2016;32(6):489-93.
11. Kodgule RR, Singh V, Dhar R, Saicharan BG, Madas SJ, Gogtay JA, et al. Reference values for peak expiratory flow in Indian adult population using a European Union scale peak flow meter. J Postgrad Med. 2014;60(2):123-9.
12. Orie NN. Comparison of normal respiratory function values in young Kenyans with those of other Africans and Caucasians. East Afr Med J. 1999;76(1):31-4.
13. Debray P, Shreevatsa BM, Roy S, Sinha S, Roy A. A comparative study of the peak expiratory flow rate of Indian and Nepalese young adults in a teaching institute. JNMA J Nepal Med Assoc. 2008;47(169):7-11.
14. Nagaldinne T, et al. Self-reported and tracker-estimated physical activity outcomes in women with chronic pelvic pain disorders: A longitudinal evaluation of construct validity. medRxiv. 2025. doi:10.1101/2025.02.10.25322006.
15. Ekelund U, Sepp H, Brage S, Becker W, Jakes R, Hennings M, et al. Criterion-related validity of the last 7-day, short form of the International Physical Activity Questionnaire in Swedish adults. Public Health Nutr. 2006;9(2):258-65.
16. von Elm E, Altman DG, Egger M, Pocock SJ, G√∏tzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61(4):344-9.
17. Du Bois D, Du Bois EF. A formula to estimate the approximate surface area if height and weight be known. Arch Intern Med. 1916;17(6):863-71.
18. Das KK, Dhundasi SA, Das SN. A study on predictors of Peak Expiratory Flow Rate in Muslim subjects (aged 18 to 20 years) of Karnataka. Indian J Physiol Pharmacol. 2002;46(3):321-7.
19. Datta S, Jha S, Jabeen J, et al. Establishing normative values of peak expiratory flow rates in the paediatric population in Jharkhand. Cureus. 2024;16(10):e72439.
20. Ritz T, Rosenfield D, Steptoe A. Physical activity, lung function, and shortness of breath in the daily life of individuals with asthma. Chest. 2010;138(4):913-8.
21. Effect of routine therapy assisted by physical exercise on pulmonary function in patients with asthma in stable stage: a systematic review and meta-analysis of randomized clinical trials. Eur J Med Res. 2022;27(1):100.
22. Menezes AM, Wehrmeister FC, Muniz LC, Pérez-Padilla R, Noal RB, Silva MC, et al. Physical activity and lung function in adolescents: the 1993 Pelotas (Brazil) birth cohort study. J Adolesc Health. 2012;51(6 Suppl):S16-21.
23. Thomas JA, Babu S, David SS, Prasad KG, James A, George AJ, et al. Peak expiratory flow profile among healthy adult individuals in Central Kerala- a cross-sectional observational study. J Res Med Dent Sci. 2021;9(11):156-61.