Smartphone-Based Estimation of Critical Walking Velocity from the Six-Minute Walk Test in Asymptomatic Adults

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Methods Thirty-five adults (60% women; 40–83 years) participated. Ten completed both the CWV protocol and M6MWT, while twenty-five performed only the M6MWT using a smartphone app to facilitate measurements of walking velocity. Results In the subsample (n = 10), mean CWV (1.49 ± 0.27 m/s) and WV₃min (1.51 ± 0.23 m/s) did not differ significantly (p > 0.05), with low variability (CV = 8.6%) and minimal bias (–0.04 m/s; 95% CI − 0.34 to + 0.41 m/s). In the larger group, WV₃min showed weak, non-significant correlations with age, anthropometric measures, and cardiovascular risk factors; however, it correlated strongly with the overall six-minute walk velocity (6MWTVel) (r = 0.83; p < 0.001). We fitted a regression model to predict WV₃min from 6MWTVel (R² = 0.689). Conclusion We may conclude that WV₃min approximates CWV with acceptable agreement and variability. This approach, supported by a smartphone application, may offer a practical, submaximal alternative for estimating sustainable walking velocity and optimizing exercise prescription in preventive and health-promotion contexts. Physiology Critical power 6MWT Cardiorespiratory fitness Exercise Smartphone Application Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Structured walking programs offer significant cardiometabolic and psychosocial benefits for asymptomatic adult populations. Regular participation in such programs is consistently associated with clinically meaningful improvements in cardiovascular fitness, as measured by increased maximum oxygen uptake (VO₂max) [ 1 ]. It is a recognized non-pharmacological intervention for the primary prevention of hypertension and dyslipidemia, resulting in reductions in both systolic and diastolic blood pressure, as well as improvements in lipid profiles [ 1 – 3 ]. Furthermore, these programs contribute to favorable changes in body composition, including reductions in body mass index and waist circumference [ 1 ]. Beyond physiological parameters, regular walking has been shown to enhance psychological well-being, with meta-analyses indicating significant decreases in symptoms of anxiety and depression, as well as improvements in overall quality of life [ 4 ]. Thus, walking is a fundamental element within public health guidelines aimed at supporting sustained health outcomes in adults without relevant pre-existing conditions. The application of the critical power (CP) model to walking yields a critical walking velocity (CWV) that distinguishes between the heavy and severe exercise intensity domains [ 5 ]. Exercise at an intensity precisely at or just below CWV represents a sustainable, steady-state workload, in which the physiological demands for energy production are in equilibrium, thereby minimizing the accumulation of metabolites such as lactate and associated neuromuscular fatigue [ 6 ]. Prescribing walking exercise at this intensity is essential, as it maximizes exercise duration and volume, key drivers of chronic physiological adaptation, while avoiding excessive strain and early exhaustion associated with the severe domain [ 6 ]. This approach is particularly beneficial for optimizing training prescriptions, as it enables individuals to accumulate substantial time in a metabolically potent yet tolerable zone, thereby efficiently stimulating improvements in aerobic capacity, metabolic health, and functional endurance with a low risk of overtraining or adverse adherence outcomes [ 7 ]. Despite the well-established utility of the CP model and the proven benefits of walking as a foundational exercise, a significant gap persists in the literature regarding the practical essential estimation of CWV in asymptomatic adult populations. While the CP concept is rigorously validated in athletic cohorts for cycling and running [ 6 , 8 ], its translation to the biomechanically distinct and universally accessible activity of walking remains underexplored in general health contexts. Current methodologies for determining CP/CWV often require repeated, exhaustive performance trials to task failure [ 5 , 9 ], protocols that are impractical, unpleasant, and potentially unsafe for non-athletic individuals in public health or clinical settings. Consequently, there is a pressing need to develop and validate simplified, submaximal testing protocols that can accurately predict CWV in these settings. Previous evidence has shown that the average walking velocity obtained in the last three minutes (WV 3 min) of an encouraged 6-minute Walk test (6MWT) agrees with the CWV assessed using four high-intensity walking tests in patients with chronic obstructive pulmonary disease [ 5 ]. Accordingly, we hypothesized that this good agreement is valid in asymptomatic adults. Thus, the objective of the present study is three-fold in asymptomatic adults: 1 – To evaluate the agreement between CWV and the WV 3 min; 2 – To develop a smartphone application to evaluate WV 3 min; and 3 – To assess the WV 3 min in a prospective cohort and evaluate its correlation with demographic, anthropometric and cardiovascular risk attributes as well as with the overall average walking velocity at 6MWT (6MWTVel). If we confirm our hypothesis, we will be able to improve the prescription of walking intensity for these subjects in health promotion walking programs. METHODS Sample and recruitment We recruited 35 participants through social media, posters at regional universities, and local print media. Inclusion criteria eligible participants in this study were adults aged 18 or older who did not have any self-reported medical diagnoses of cardiopulmonary disease, locomotor disorders, or electrocardiographic abnormalities at rest or during exertion. Additionally, they must have had no other conditions that would prevent them from performing physical exercise safely. Exclusion criteria spirometry results indicating obstructive ventilatory disturbance (defined as a forced expiratory volume in 1 second to forced vital capacity ratio of less than 0.7) after a forced vital capacity maneuver performed with a calibrated spirometer (Quark PFT, COSMED) following American Thoracic Society guidelines [ 10 ]. Also, severe arrhythmias at rest or potentially life-threatening during exercise testing, and signs or symptoms of ischemia during a symptom-limited ergometric treadmill test (ATL, Inbrasport, Curitiba, Brazil) using a ramp protocol with continuous stress electrocardiography. Ten participants completed a series of timed walking assessments as follows: (1) two encouraged modified six-minute walk tests (M6MWT) conducted on a 30-meter hallway; (2) a standard incremental shuttle walk test (ISWT) [ 11 ]; (3) four high-intensity, constant-speed walking tests at varying speeds to determine CWV, administered in random order on separate days; and (4) an additional constant-speed walking test at CWV, intended to assess sustained performance for a minimum duration of 20 minutes. The remaining 25 participants completed only the M6MWT to investigate correlations between WV 3 min and demographic, anthropometric, and cardiovascular risk attributes. Additionally, we investigated the correlation between 6MWTVel and WV 3 min. We developed a smartphone application to register the partial distances throughout the test and estimate walking velocities. The ethics committee of our university approved this study. All participants provided informed consent before participating in the study. Clinical and sociodemographic evaluation We investigated self-reported medical diagnoses related to cardiovascular disease risk factors, including age (≥ 45 for men, ≥ 55 for women), hypertension, diabetes, dyslipidemia, family history of premature coronary heart disease (myocardial infarction or sudden death before age 55 for male relatives or age 65 for female relatives), smoking status, and physical activity level. We registered self-reported physical activity status using a single question on whether participants performed at least 150 minutes per week of moderate-to-vigorous physical activity or at least 75 minutes per week of vigorous physical activity [ 12 ]. Additionally, physical activity scores were collected from the subjects using the Baecke questionnaire [ 13 ], and subjects were classified as insufficiently active (score < 8) or physically active but still untrained (score ≥ 8). Anthropometric assessment Body weight and height were measured using a calibrated digital scale equipped with a stadiometer (Toledo Prix 2096PP, Brazil). Body mass index (BMI) was calculated in kg/m², and obesity was defined as having a BMI of 30 kg/m² or higher. Incremental Shuttle Walk Test and Maximal Walking Speed An Incremental Shuttle Walk Test was conducted in a 10-meter indoor hallway, with walking speed increased by 0.17 m/s every minute, following the method described by Singh et al. [ 11 ]. Traffic cones were placed 0.5 meters from each end to minimize directional changes. The test began with a 5-second tone, followed by 3-second tones signaling the subject to change direction and a 5-second tone every 60 seconds to increase pace. The test ended when the subject could not reach the nearest cone (more than 0.5 meters away) or chose to stop. Dyspnea and leg fatigue were assessed before and after each of the three tests using the Borg CR10 scale [ 14 ], with 20 minutes of rest between tests and verbal encouragement every minute. The incremental shuttle walk distance (ISWD) was recorded and expressed as a percentage of predicted values [ 15 ]. The maximum walking velocity in Km/h was determined by the highest ISWD achieved. Modified Six-Minute Walk Test We conducted the modified six-minute walk test (M6MWT) according to the guidelines established by the American Thoracic Society and the European Respiratory Society [ 16 ]. Previous research indicates that asymptomatic individuals do not exhibit a learning effect with the 6-minute walk test (6MWT) [ 17 ]. However, to ensure accuracy, we performed two tests for each participant, spaced 30 minutes apart, and used the best performance as the six-minute walk distance (6MWD) score for this study. Participants were instructed to walk as far as they could in six minutes along a flat, straight corridor that was 30 meters long, marked at every 3 meters. Two traffic cones bordered the course. Standardized instructions and verbal encouragement were given at one-minute intervals [ 16 ]. We recorded the distance covered during the 6MWT in meters, along with the percentage of predicted values [ 17 ]. We modified the test by recording the distances covered each minute of walking. Using these distances, we calculated the partial distances per minute in kilometers per hour. The average walking speed over the last three minutes of walking (WV 3 min) was calculated to assess its reliability and agreement regarding the CWV. Critical walking velocity Four high-intensity walking protocols were conducted at 90%, 95%, 100%, and 105% of peak walking speed as determined by the incremental shuttle test. For each participant, the CWV was established as the asymptote of a hyperbolic function describing the relationship between walking speed and time to exhaustion. When plotting the reciprocal of time to exhaustion, this association becomes linear. The CWS value was calculated using the y-intercept, as proposed by Neder et al. [ 18 ]. Figure 1 presents a typical example of the CWV determination. Smartphone Application Development To enhance translation of findings into practice, we developed a free Android application, the Six-minute Walk Speed App (SMWS App), using the Flutter framework and Android Studio (v.2024.2). The app includes three modules: input interface, computation module with predictive equations, and output visualization ( Supplementary File 1 ). Apart from the walking velocities and distances, the app provides a report with the estimated VO 2 max [ 19 ] and the percentage of predicted 6MWD [ 17 ]. Statistical Analysis Continuous variables are expressed as mean ± SD, and dichotomous ones are presented as percentages. Reliability and agreement between CWV and WV₃min were tested using paired t-tests, coefficient of variation (CV), and Bland–Altman plots. Associations with demographic, anthropometric, and cardiovascular risk variables were assessed using Pearson correlations and multiple linear regression among continuous variables. As for dichotomous variables, we calculated Poin-biserial correlation [ 20 ]. We considered factor 0 for females and 1 for males, and 0 for absence and 1 for presence of the classic cardiovascular risk factors. We considered p < 0.05 as statistically significant for all tests. RESULTS A total of 35 asymptomatic adults (60% women; age 40 to 83 years; 26.3 ± 4.7 kg/m²) completed the study (Table 1 ). The characteristics of the 10 participants who underwent the CWV protocol did not differ from those of the other 25 participants who only performed the M6MWT in the correlation analysis (Table 2 ). Table 1 General characteristics of the studied sample (n = 35) Variables Results Age (years) 59 ± 12 Sex Males (%) 48 Females (%) 52 Weight (kg) 73.9 ± 19.4 Height (m) 1.67 ± 0.11 Body mass index (kg/m 2 ) 26.3 ± 4.7 Cardiovascular risk Arterial hypertension (%) 20.8 Diabetes (%) 13.0 Dyslipidemia (%) 41.7 Obesity (%) 17.4 Current smoking (%) 8.3 Insufficient physical activity (%) 16.7 M6MWT (m) 578 ± 79 M6MWT (% pred.) 107 ± 14 6MWTVel (m/s) 1.60 ± 0.22 WV 3 min (m/s) 1.59 ± 0.25 M6MWT: modified six-minute walk test; 6MWTVel: overall average walking velocity during M6MWT; WV 3 min: average walking velocity at the last three minutes of the M6MWT. Table 2 General characteristics of the sample submitted to the critical walking velocity assessment protocol (n = 10) Variables Results Age (years) 61 ± 17 Sex Males (%) 40 Females (%) 60 Weight (kg) 71.0 ± 13.3 Height (m) 1.69 ± 0.55 Body mass index (kg/m 2 ) 25.4 ± 3.7 Cardiovascular risk Arterial hypertension (%) 18.6 Diabetes (%) 12.9 Dyslipidemia (%) 37.4 Obesity (%) 15.9 Current smoking (%) 7.8 Insufficient physical activity (%) 15.1 ISWT (m) ISWT (% pred.) Maximal walking velocity (m/s) 1.72 ± 0.38 M6MWT (m) 594 ± 99 M6MWT (% pred.) 112 ± 20 6MWTVel (m/s) 1.65 ± 0.39 WV 3 min (m/s) 1.51 ± 0.23 CWV (m/s) 1.49 ± 0.27 M6MWT: modified six-minute walk test; 6MWTVel: overall average walking velocity during M6MWT; WV 3 min: average walking velocity at the last three minutes of the M6MWT; CWV: critical walking velocity. In the sample of 10 participants, mean CWV was 1.49 ± 0.27 m/s and mean WV₃min was 1.51 ± 0.23 m/s (p > 0.05). The CV was 8.6% and the Bland–Altman analysis showed a small bias (mean difference, − 0.04 m/s: 95% confidence interval, -0.34 to + 0.41) (Fig. 2 ). In the remaining 25 participants who underwent only the M6MWT using the SMWS App, the walking velocity kinetics showed an increase at the beginning of the M6MWT, with the highest value at two minutes, followed by a decrease at three minutes, and then becoming very stable in the last three minutes of the test (Fig. 3 ). We found weak non-significant (p > 0.05) correlations between WV₃min and age (r = -0.34), weight (r = 0.17), height (r = 0.26), and BMI (r = 0.13). Likewise, Point-biserial correlations between WV₃min and sex (r = 0.32), hypertension (r = 0.02), diabetes (r = -0.23), dyslipidemia (r = 0.29), obesity (r = 0.05), smoking (r = -0.32), and insufficient physical activity (r = -0.15) were non-significant (p > 0.05). On the other hand, we found a strong correlation between WV₃min and the 6MWTVel (Fig. 4 ). Thus, we fitted a simple linear regression to estimate WV₃min based on 6MWTVel (Table 3 ). Table 3 Results of the simple linear regression with the prediction of the average walking velocity at the last three minutes of the six-minute walk test (WV3min) by the overall average walking velocity during the test (6MWTVel). Model Unstandardized Coefficients Standardized Coefficients P 95.0% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound (Constant) 0.035 0.223 0.877 -0.428 0.498 6MWTVel (m/s) 0.962 0.138 0.830 0.000 0.677 1.248 Dependent variable: 6MWTVel (m/s); R 2 = 0.689; Std. Error of the Estimate = 0.145 DISCUSSION Our findings contribute to the growing literature on walking-based field tests by demonstrating the practical utility of the last three-minute walking velocity from an M6MWT as an estimator of CWV in asymptomatic adults. This aligns with prior work with patients with chronic obstructive pulmonary disease showing that an encouraged 6MWT produced oxygen uptake, ventilation, and walking speeds comparable to those at CWV derived from exhaustion walking tests [ 5 ]. Our study extends that concept into an apparently healthy adult population, showing very close numerical agreement (mean difference − 0.04 m/s) and acceptable variability (CV 8.6%). The strong correlation between WV₃min and 6MWTVel (r = 0.83) further supports the construct validity of our method, as it appears to capture the steady-state, sustainable walking speed at the end of the test, a time when fatigue and deceleration tendencies tend to stabilize (Fig. 3 ). Indeed, minute-by-minute analyses of the 6MWT in other populations (e.g., multiple sclerosis) showed that gait speed trajectories stabilize or decelerate after the midpoint of the test [ 21 ]. By using the average from the last three minutes, our approach offers a pragmatic simplification of more elaborate protocols used to compute CWV. From a theoretical perspective, the use of CWV (analogous to critical power) represents a boundary between intensities where a steady physiological state is possible, and higher intensities where fatigue accumulates rapidly [ 22 ]. Walking just below this boundary optimizes exercise duration, volume, and adaptation potential with minimal risk of overexertion [ 23 ]. While the CP concept has been widely applied to cycling and running, translation to walking remains less common [ 24 ]. Our results suggest that translation can be advanced by providing a simple, field-based alternative to exhaustive, constant-speed tests. We found that the WV₃min was only weakly and non-significantly correlated with demographic/anthropometric (age, height, weight) and cardiovascular risk attributes (hypertension, dyslipidemia, smoking, physical inactivity). This suggests that the CWV may isolate sustainable walking mechanics rather than being confounded by those risk factors, at least in this asymptomatic cohort. In line with previous data demonstrating a low impact of age on gait speed in healthy adults [ 25 ] and findings that walking pace has prognostic value independently of cardiovascular risk factors [ 26 ], our results suggest that the WV₃min/CWV may isolate a more mechanistic or functional aspect of sustained gait, and not simply reflect the demographic or cardiovascular risk profile. From a clinical/practical standpoint, this means the WV₃min could be broadly applied without needing extensive stratification by demographic or risk-factor subgroups. Furthermore, the development of a smartphone application to compute estimated CWV from the M6MWT (and its associated predictive equation) enhances the translational value: practitioners can prescribe walking intensity based on the individual’s own test results rather than relying on normative tables or generalized estimates. Some limitations warrant consideration. Our CWV sub-group was small (n = 10) and comprised relatively healthy adults; therefore, extrapolation to older, frailer, or clinical populations should be made cautiously. The agreement statistics (bias and limits of agreement) should be further validated in larger, heterogeneous samples. Additionally, although we demonstrate cross-sectional agreement, we did not investigate whether walking training prescribed at WV₃min yields better outcomes than standard prescriptions. Finally, some correlations with WV₃min were moderate despite being non-significant, indicating a potential beta error due to the small sample size. There is evidence that non-exhaustive tests can predict CP, race performance, or related physiological/anthropometric measures — but prediction precision is moderate and depends on the cohort and method [ 27 ]. However, although CP may be predicted by non-exhaustive exercise tests [ 27 ], apart from lean body mass or muscle girth [ 24 , 28 ], it has been shown that it is also predicted weekly by non-exercise attributes, such as those investigated in the present study. Non-exercise factors, such as age, sex, body size, and BMI, can influence aerobic capacity and possibly CP; however, large-scale studies reporting simple correlations between CP and these predictors are lacking. Most current research targets physiological factors (VO₂max, W’, muscle phenotype), not demographic or anthropometric ones. Future work should validate the findings over time, assess the training effects, and explore the cardiovascular and metabolic implications. In summary, we may conclude that an encouraged M6MWT with calculation of WV₃min provides a valid, low-burden method for estimating CWV in asymptomatic adults, enabling the targeted prescription of walking exercise intensity for health-promotion programs. The WV₃min offers a reliable and straightforward estimate of maximal sustainable walking velocity in asymptomatic adults. As it is easily obtained and not strongly influenced by common demographic or cardiovascular risk factors, WV₃min can be used to guide safe and effective walking exercise prescriptions. The accompanying smartphone app offers a practical tool for health professionals to personalize walking intensity, supporting the broader implementation of optimized walking programs in health promotion and primary prevention. Abbreviations WV₃min average walking velocity during the last three minutes M6MWT modified six-minute walk test CWV critical walking velocity 6MWTVel overall six-minute walk velocity 6MWT six-minute walk test VO2max maximum oxygen uptake CP critical power ISWT incremental shuttle walk test BMI body mass index ISWD incremental shuttle walk distance 6MWD six-minute walk distance 6MWS App Six-minute Walk Speed App CV coefficient of variation Declarations Ethics approval and consent to participate The Federal University of São Paulo Ethics Committee approved the study (#186.796) Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are not publicly available for the following reasons: 1. The are part of a cohort study. There are ongoing analysis being conducted. The results will be submitted for publication shortly and then publicized. However, our data are available from the corresponding author upon reasonable request. Competing interests Transparency is a cornerstone of our research. We want to assure our readers that none of the authors has any conflict of interest, ensuring the integrity and impartiality of our findings. Authors’ contributions VD had full access to the study's data and took responsibility for its integrity and accuracy. VZD also conducted data analysis and wrote the first version of the manuscript. TO substantially contributed to the study design and data interpretation. All the authors approved the final version of the manuscript. VD was mainly responsible for the data management in the RedCap platform. Finaly, VD developed the Six-Minute Walk Speed App using Flutter framework and Dart programming language. Funding The Sao Paulo Research Foundation funded this Study, grant #2011/07282-6; Acknowledgments We are grateful for the financial support that made these studies possible as detailed in the Declarations Section. The Sao Paulo Research Foundation (FAPESP) funded the development of walking tests. References Murtagh EM, Nichols L, Mohammed MA et al (2015) The effect of walking on risk factors for cardiovascular disease: An updated systematic review and meta-analysis of randomised control trials. Prev Med (Baltim) ; 72 Piercy KL, Troiano RP (2018) Physical Activity Guidelines for Americans From the US Department of Health and Human Services. Circ Cardiovasc Qual Outcomes 11. 10.1161/CIRCOUTCOMES.118.005263 Mann S, Beedie C, Jimenez A (2014) Differential effects of aerobic exercise, resistance training and combined exercise modalities on cholesterol and the lipid profile: review, synthesis and recommendations. 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J Strength Cond Res 22. 10.1519/JSC.0b013e31816a41fa Chorley A, Bott RP, Marwood S et al (2020) Physiological and anthropometric determinants of critical power, W′ and the reconstitution of W′ in trained and untrained male cyclists. Eur J Appl Physiol 120. 10.1007/s00421-020-04459-6 Additional Declarations The authors declare no competing interests. Supplementary Files Spplementaryfile1Final.pdf Cite Share Download PDF Status: Posted Version 1 posted 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-9558754","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631366686,"identity":"4a57d5f8-6435-4510-8e6b-6fd9a942b105","order_by":0,"name":"Victor Zuniga Dourado","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACZgjF2AAieRhsgCKMD0DsBDxawKphWtKAIswG+LUwoGo5DDIFvxaD48zPH3zcwyDbL5H78MObivOJ29mZGR/+3MGQZ96AQ8thNsPGGc8YjGfOSDeWnHPmduLOZmZmY94zDMUyB7BrkWzmYWzmOcCQuOFGGoM0b9vtxA2H+Y9JM7YxJM7A4TBkLcy/edvOAbUws//8iUcLPzNCCxvQlgMgLWwMvHi1sBnOnHFAwnhmzzM2yzlnko2BWpilec9IFEvg0MLGf/jBhw8HbGT72dOYb7ypsJPdcP4w48efO2zycGmBAnRpxgYCGjABJGZHwSgYBaNgFIABAHW/WCC3fCSkAAAAAElFTkSuQmCC","orcid":"","institution":"Federal University of São Paulo (UNIFESP)","correspondingAuthor":true,"prefix":"","firstName":"Victor","middleName":"Zuniga","lastName":"Dourado","suffix":""},{"id":631368565,"identity":"218ab21c-2846-4b93-adde-2d4eee654f8e","order_by":1,"name":"Thatiane Ostolin","email":"","orcid":"","institution":"Federal University of São Paulo (UNIFESP)","correspondingAuthor":false,"prefix":"","firstName":"Thatiane","middleName":"","lastName":"Ostolin","suffix":""}],"badges":[],"createdAt":"2026-04-29 00:04:19","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9558754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9558754/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108159836,"identity":"37116913-e543-416f-89f7-74cbbfbd0b2e","added_by":"auto","created_at":"2026-04-30 03:52:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38221,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the calculation of critical walking velocity (CWS) in one of the participants, whose maximum speed obtained in an incremental shuttle walk test (ISWT) was 1.72 m/s. We performed four high-intensity walking tests at constant speeds controlled by a set of pre-recorded audios (V1 = 1.54 m/s; V2 = 1.63 m/s; V3 = 1.72 m/s; V4 = 1.80 m/s) corresponding to 90, 95, 100, and 105% of the maximum speed obtained in the incremental shuttle walk test. The CWV can be calculated using the asymptote of the hyperbolic relationship obtained between walking speed (y-axis) and the time required until exhaustion (140 to 1050 s in this example) (A) or it can be calculated more easily using the linear relationship between walking speed (y-axis) and the reciprocal of time (1/t; x-axis), where the CWV is equivalent to the y-intercept of a linear regression equation y = ax + b (B). In this example, the CWV obtained was 1.496 m/s.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9558754/v1/757360920cfe909620d5d504.jpg"},{"id":108182962,"identity":"4f0899b4-3cc6-4978-b0c0-736f5161bc18","added_by":"auto","created_at":"2026-04-30 08:59:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36341,"visible":true,"origin":"","legend":"\u003cp\u003eThe Bland-Altman plot with limits of agreement between the average walking velocity during the last three minutes (WV₃min) of an encouraged modified six-minute walk test and the critical walking velocity (CWV) obtained as described in Figure 1.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9558754/v1/125c95339a7265d0bc218656.jpg"},{"id":108159840,"identity":"f6eeff12-d412-4cae-9684-e05ca482148f","added_by":"auto","created_at":"2026-04-30 03:52:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24465,"visible":true,"origin":"","legend":"\u003cp\u003eWalking velocity kinetics throughout the modified six-minute walk test (M6MWT) in 25 participants who performed the test in the validation approach (1-min = 1.57 ± 0.61 m/s; 2-min = 1.77 ± 0.70 m/s; 3-min = 1.54 ± 0.57 m/s; 4-min = 1.57 ± 0.44 m/s; 5-min = 1.58 ± 0.37 m/s; and 6-min = 1.58 ± 0.38 m/s). It shows the evident stabilization and lower standard deviation during the last three minutes of the test.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9558754/v1/0b662b6aa6b386c8b3561881.jpg"},{"id":108159839,"identity":"8217fd4c-97f2-499b-b24b-b9f069adce40","added_by":"auto","created_at":"2026-04-30 03:52:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23617,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the overall six-minute walk average velocity (6MWTVel) and the average walking velocity during the last three minutes (WV₃min) of an encouraged modified six-minute walk test, both in meters per second (r = 0.83; p \u0026lt; 0.001). The following equation can predict the WV₃min: WV₃min = 0.035 + (0.962 x 6MWTVel).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9558754/v1/3428cbed7ea06e4b1433e3b7.jpg"},{"id":108183530,"identity":"1c2cd7c3-2627-4f6b-b6e8-e90400ddc82b","added_by":"auto","created_at":"2026-04-30 09:01:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":405302,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9558754/v1/bcd790d5-bc8e-4475-96cb-ce469557cea6.pdf"},{"id":108182511,"identity":"4fb7a2e0-3d20-42f3-8e28-1f91dc3a00de","added_by":"auto","created_at":"2026-04-30 08:59:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1923738,"visible":true,"origin":"","legend":"","description":"","filename":"Spplementaryfile1Final.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9558754/v1/22209b47b090c63805214bdb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSmartphone-Based Estimation of Critical Walking Velocity from the Six-Minute Walk Test in Asymptomatic Adults\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eStructured walking programs offer significant cardiometabolic and psychosocial benefits for asymptomatic adult populations. Regular participation in such programs is consistently associated with clinically meaningful improvements in cardiovascular fitness, as measured by increased maximum oxygen uptake (VO₂max) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is a recognized non-pharmacological intervention for the primary prevention of hypertension and dyslipidemia, resulting in reductions in both systolic and diastolic blood pressure, as well as improvements in lipid profiles [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, these programs contribute to favorable changes in body composition, including reductions in body mass index and waist circumference [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Beyond physiological parameters, regular walking has been shown to enhance psychological well-being, with meta-analyses indicating significant decreases in symptoms of anxiety and depression, as well as improvements in overall quality of life [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, walking is a fundamental element within public health guidelines aimed at supporting sustained health outcomes in adults without relevant pre-existing conditions.\u003c/p\u003e \u003cp\u003eThe application of the critical power (CP) model to walking yields a critical walking velocity (CWV) that distinguishes between the heavy and severe exercise intensity domains [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Exercise at an intensity precisely at or just below CWV represents a sustainable, steady-state workload, in which the physiological demands for energy production are in equilibrium, thereby minimizing the accumulation of metabolites such as lactate and associated neuromuscular fatigue [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Prescribing walking exercise at this intensity is essential, as it maximizes exercise duration and volume, key drivers of chronic physiological adaptation, while avoiding excessive strain and early exhaustion associated with the severe domain [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This approach is particularly beneficial for optimizing training prescriptions, as it enables individuals to accumulate substantial time in a metabolically potent yet tolerable zone, thereby efficiently stimulating improvements in aerobic capacity, metabolic health, and functional endurance with a low risk of overtraining or adverse adherence outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the well-established utility of the CP model and the proven benefits of walking as a foundational exercise, a significant gap persists in the literature regarding the practical essential estimation of CWV in asymptomatic adult populations. While the CP concept is rigorously validated in athletic cohorts for cycling and running [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], its translation to the biomechanically distinct and universally accessible activity of walking remains underexplored in general health contexts. Current methodologies for determining CP/CWV often require repeated, exhaustive performance trials to task failure [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], protocols that are impractical, unpleasant, and potentially unsafe for non-athletic individuals in public health or clinical settings. Consequently, there is a pressing need to develop and validate simplified, submaximal testing protocols that can accurately predict CWV in these settings.\u003c/p\u003e \u003cp\u003ePrevious evidence has shown that the average walking velocity obtained in the last three minutes (WV\u003csub\u003e3\u003c/sub\u003emin) of an encouraged 6-minute Walk test (6MWT) agrees with the CWV assessed using four high-intensity walking tests in patients with chronic obstructive pulmonary disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Accordingly, we hypothesized that this good agreement is valid in asymptomatic adults. Thus, the objective of the present study is three-fold in asymptomatic adults: 1 \u0026ndash; To evaluate the agreement between CWV and the WV\u003csub\u003e3\u003c/sub\u003emin; 2 \u0026ndash; To develop a smartphone application to evaluate WV\u003csub\u003e3\u003c/sub\u003emin; and 3 \u0026ndash; To assess the WV\u003csub\u003e3\u003c/sub\u003emin in a prospective cohort and evaluate its correlation with demographic, anthropometric and cardiovascular risk attributes as well as with the overall average walking velocity at 6MWT (6MWTVel). If we confirm our hypothesis, we will be able to improve the prescription of walking intensity for these subjects in health promotion walking programs.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample and recruitment\u003c/h2\u003e \u003cp\u003e We recruited 35 participants through social media, posters at regional universities, and local print media.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInclusion criteria\u003c/strong\u003e \u003cp\u003eeligible participants in this study were adults aged 18 or older who did not have any self-reported medical diagnoses of cardiopulmonary disease, locomotor disorders, or electrocardiographic abnormalities at rest or during exertion. Additionally, they must have had no other conditions that would prevent them from performing physical exercise safely.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExclusion criteria\u003c/strong\u003e \u003cp\u003espirometry results indicating obstructive ventilatory disturbance (defined as a forced expiratory volume in 1 second to forced vital capacity ratio of less than 0.7) after a forced vital capacity maneuver performed with a calibrated spirometer (Quark PFT, COSMED) following American Thoracic Society guidelines [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Also, severe arrhythmias at rest or potentially life-threatening during exercise testing, and signs or symptoms of ischemia during a symptom-limited ergometric treadmill test (ATL, Inbrasport, Curitiba, Brazil) using a ramp protocol with continuous stress electrocardiography.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTen participants completed a series of timed walking assessments as follows: (1) two encouraged modified six-minute walk tests (M6MWT) conducted on a 30-meter hallway; (2) a standard incremental shuttle walk test (ISWT) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]; (3) four high-intensity, constant-speed walking tests at varying speeds to determine CWV, administered in random order on separate days; and (4) an additional constant-speed walking test at CWV, intended to assess sustained performance for a minimum duration of 20 minutes.\u003c/p\u003e \u003cp\u003eThe remaining 25 participants completed only the M6MWT to investigate correlations between WV\u003csub\u003e3\u003c/sub\u003emin and demographic, anthropometric, and cardiovascular risk attributes. Additionally, we investigated the correlation between 6MWTVel and WV\u003csub\u003e3\u003c/sub\u003emin. We developed a smartphone application to register the partial distances throughout the test and estimate walking velocities.\u003c/p\u003e \u003cp\u003eThe ethics committee of our university approved this study. All participants provided informed consent before participating in the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical and sociodemographic evaluation\u003c/h3\u003e\n\u003cp\u003eWe investigated self-reported medical diagnoses related to cardiovascular disease risk factors, including age (\u0026ge;\u0026thinsp;45 for men, \u0026ge; 55 for women), hypertension, diabetes, dyslipidemia, family history of premature coronary heart disease (myocardial infarction or sudden death before age 55 for male relatives or age 65 for female relatives), smoking status, and physical activity level.\u003c/p\u003e \u003cp\u003eWe registered self-reported physical activity status using a single question on whether participants performed at least 150 minutes per week of moderate-to-vigorous physical activity or at least 75 minutes per week of vigorous physical activity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, physical activity scores were collected from the subjects using the Baecke questionnaire [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and subjects were classified as insufficiently active (score\u0026thinsp;\u0026lt;\u0026thinsp;8) or physically active but still untrained (score\u0026thinsp;\u0026ge;\u0026thinsp;8).\u003c/p\u003e\n\u003ch3\u003eAnthropometric assessment\u003c/h3\u003e\n\u003cp\u003eBody weight and height were measured using a calibrated digital scale equipped with a stadiometer (Toledo Prix 2096PP, Brazil). Body mass index (BMI) was calculated in kg/m\u0026sup2;, and obesity was defined as having a BMI of 30 kg/m\u0026sup2; or higher.\u003c/p\u003e\n\u003ch3\u003eIncremental Shuttle Walk Test and Maximal Walking Speed\u003c/h3\u003e\n\u003cp\u003eAn Incremental Shuttle Walk Test was conducted in a 10-meter indoor hallway, with walking speed increased by 0.17 m/s every minute, following the method described by Singh et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Traffic cones were placed 0.5 meters from each end to minimize directional changes.\u003c/p\u003e \u003cp\u003e The test began with a 5-second tone, followed by 3-second tones signaling the subject to change direction and a 5-second tone every 60 seconds to increase pace. The test ended when the subject could not reach the nearest cone (more than 0.5 meters away) or chose to stop.\u003c/p\u003e \u003cp\u003eDyspnea and leg fatigue were assessed before and after each of the three tests using the Borg CR10 scale [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], with 20 minutes of rest between tests and verbal encouragement every minute. The incremental shuttle walk distance (ISWD) was recorded and expressed as a percentage of predicted values [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The maximum walking velocity in Km/h was determined by the highest ISWD achieved.\u003c/p\u003e\n\u003ch3\u003eModified Six-Minute Walk Test\u003c/h3\u003e\n\u003cp\u003eWe conducted the modified six-minute walk test (M6MWT) according to the guidelines established by the American Thoracic Society and the European Respiratory Society [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previous research indicates that asymptomatic individuals do not exhibit a learning effect with the 6-minute walk test (6MWT) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, to ensure accuracy, we performed two tests for each participant, spaced 30 minutes apart, and used the best performance as the six-minute walk distance (6MWD) score for this study.\u003c/p\u003e \u003cp\u003eParticipants were instructed to walk as far as they could in six minutes along a flat, straight corridor that was 30 meters long, marked at every 3 meters. Two traffic cones bordered the course. Standardized instructions and verbal encouragement were given at one-minute intervals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. We recorded the distance covered during the 6MWT in meters, along with the percentage of predicted values [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe modified the test by recording the distances covered each minute of walking. Using these distances, we calculated the partial distances per minute in kilometers per hour. The average walking speed over the last three minutes of walking (WV\u003csub\u003e3\u003c/sub\u003emin) was calculated to assess its reliability and agreement regarding the CWV.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCritical walking velocity\u003c/h2\u003e \u003cp\u003eFour high-intensity walking protocols were conducted at 90%, 95%, 100%, and 105% of peak walking speed as determined by the incremental shuttle test. For each participant, the CWV was established as the asymptote of a hyperbolic function describing the relationship between walking speed and time to exhaustion. When plotting the reciprocal of time to exhaustion, this association becomes linear. The CWS value was calculated using the y-intercept, as proposed by Neder et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a typical example of the CWV determination.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSmartphone Application Development\u003c/h3\u003e\n\u003cp\u003eTo enhance translation of findings into practice, we developed a free Android application, the Six-minute Walk Speed App (SMWS App), using the Flutter framework and Android Studio (v.2024.2). The app includes three modules: input interface, computation module with predictive equations, and output visualization (\u003cb\u003eSupplementary File 1\u003c/b\u003e). Apart from the walking velocities and distances, the app provides a report with the estimated VO\u003csub\u003e2\u003c/sub\u003emax [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and the percentage of predicted 6MWD [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, and dichotomous ones are presented as percentages. Reliability and agreement between CWV and WV₃min were tested using paired t-tests, coefficient of variation (CV), and Bland\u0026ndash;Altman plots. Associations with demographic, anthropometric, and cardiovascular risk variables were assessed using Pearson correlations and multiple linear regression among continuous variables. As for dichotomous variables, we calculated Poin-biserial correlation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We considered factor 0 for females and 1 for males, and 0 for absence and 1 for presence of the classic cardiovascular risk factors. We considered p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as statistically significant for all tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 35 asymptomatic adults (60% women; age 40 to 83 years; 26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 kg/m\u0026sup2;) completed the study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The characteristics of the 10 participants who underwent the CWV protocol did not differ from those of the other 25 participants who only performed the M6MWT in the correlation analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics of the studied sample (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 \u0026plusmn; 12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \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=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.9 \u0026plusmn; 19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67 \u0026plusmn; 0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.3 \u0026plusmn; 4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial hypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficient physical activity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM6MWT (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e578 \u0026plusmn; 79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM6MWT (% pred.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 \u0026plusmn; 14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6MWTVel (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 \u0026plusmn; 0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWV\u003csub\u003e3\u003c/sub\u003emin (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59 \u0026plusmn; 0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eM6MWT: modified six-minute walk test; 6MWTVel: overall average walking velocity during M6MWT; WV\u003csub\u003e3\u003c/sub\u003emin: average walking velocity at the last three minutes of the M6MWT.\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=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics of the sample submitted to the critical walking velocity assessment protocol (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResults\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 \u0026plusmn; 17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \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=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.0 \u0026plusmn; 13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69 \u0026plusmn; 0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.4 \u0026plusmn; 3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial hypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficient physical activity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISWT (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISWT (% pred.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximal walking velocity (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72 \u0026plusmn; 0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM6MWT (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e594 \u0026plusmn; 99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM6MWT (% pred.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 \u0026plusmn; 20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6MWTVel (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65 \u0026plusmn; 0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWV\u003csub\u003e3\u003c/sub\u003emin (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51 \u0026plusmn; 0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCWV (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49 \u0026plusmn; 0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eM6MWT: modified six-minute walk test; 6MWTVel: overall average walking velocity during M6MWT; WV\u003csub\u003e3\u003c/sub\u003emin: average walking velocity at the last three minutes of the M6MWT; CWV: critical walking velocity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the sample of 10 participants, mean CWV was 1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 m/s and mean WV₃min was 1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 m/s (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The CV was 8.6% and the Bland\u0026ndash;Altman analysis showed a small bias (mean difference, \u0026minus;\u0026thinsp;0.04 m/s: 95% confidence interval, -0.34 to +\u0026thinsp;0.41) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the remaining 25 participants who underwent only the M6MWT using the SMWS App, the walking velocity kinetics showed an increase at the beginning of the M6MWT, with the highest value at two minutes, followed by a decrease at three minutes, and then becoming very stable in the last three minutes of the test (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We found weak non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) correlations between WV₃min and age (r = -0.34), weight (r\u0026thinsp;=\u0026thinsp;0.17), height (r\u0026thinsp;=\u0026thinsp;0.26), and BMI (r\u0026thinsp;=\u0026thinsp;0.13). Likewise, Point-biserial correlations between WV₃min and sex (r\u0026thinsp;=\u0026thinsp;0.32), hypertension (r\u0026thinsp;=\u0026thinsp;0.02), diabetes (r = -0.23), dyslipidemia (r\u0026thinsp;=\u0026thinsp;0.29), obesity (r\u0026thinsp;=\u0026thinsp;0.05), smoking (r = -0.32), and insufficient physical activity (r = -0.15) were non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, we found a strong correlation between WV₃min and the 6MWTVel (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Thus, we fitted a simple linear regression to estimate WV₃min based on 6MWTVel (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the simple linear regression with the prediction of the average walking velocity at the last three minutes of the six-minute walk test (WV3min) by the overall average walking velocity during the test (6MWTVel).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95.0% Confidence Interval for B\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6MWTVel (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eDependent variable: 6MWTVel (m/s); R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.689; Std. Error of the Estimate\u0026thinsp;=\u0026thinsp;0.145\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur findings contribute to the growing literature on walking-based field tests by demonstrating the practical utility of the last three-minute walking velocity from an M6MWT as an estimator of CWV in asymptomatic adults. This aligns with prior work with patients with chronic obstructive pulmonary disease showing that an encouraged 6MWT produced oxygen uptake, ventilation, and walking speeds comparable to those at CWV derived from exhaustion walking tests [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Our study extends that concept into an apparently healthy adult population, showing very close numerical agreement (mean difference \u0026minus;\u0026thinsp;0.04 m/s) and acceptable variability (CV 8.6%).\u003c/p\u003e \u003cp\u003eThe strong correlation between WV₃min and 6MWTVel (r\u0026thinsp;=\u0026thinsp;0.83) further supports the construct validity of our method, as it appears to capture the steady-state, sustainable walking speed at the end of the test, a time when fatigue and deceleration tendencies tend to stabilize (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Indeed, minute-by-minute analyses of the 6MWT in other populations (e.g., multiple sclerosis) showed that gait speed trajectories stabilize or decelerate after the midpoint of the test [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. By using the average from the last three minutes, our approach offers a pragmatic simplification of more elaborate protocols used to compute CWV.\u003c/p\u003e \u003cp\u003eFrom a theoretical perspective, the use of CWV (analogous to critical power) represents a boundary between intensities where a steady physiological state is possible, and higher intensities where fatigue accumulates rapidly [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Walking just below this boundary optimizes exercise duration, volume, and adaptation potential with minimal risk of overexertion [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. While the CP concept has been widely applied to cycling and running, translation to walking remains less common [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our results suggest that translation can be advanced by providing a simple, field-based alternative to exhaustive, constant-speed tests.\u003c/p\u003e \u003cp\u003eWe found that the WV₃min was only weakly and non-significantly correlated with demographic/anthropometric (age, height, weight) and cardiovascular risk attributes (hypertension, dyslipidemia, smoking, physical inactivity). This suggests that the CWV may isolate sustainable walking mechanics rather than being confounded by those risk factors, at least in this asymptomatic cohort. In line with previous data demonstrating a low impact of age on gait speed in healthy adults [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and findings that walking pace has prognostic value independently of cardiovascular risk factors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], our results suggest that the WV₃min/CWV may isolate a more mechanistic or functional aspect of sustained gait, and not simply reflect the demographic or cardiovascular risk profile. From a clinical/practical standpoint, this means the WV₃min could be broadly applied without needing extensive stratification by demographic or risk-factor subgroups.\u003c/p\u003e \u003cp\u003eFurthermore, the development of a smartphone application to compute estimated CWV from the M6MWT (and its associated predictive equation) enhances the translational value: practitioners can prescribe walking intensity based on the individual\u0026rsquo;s own test results rather than relying on normative tables or generalized estimates.\u003c/p\u003e \u003cp\u003eSome limitations warrant consideration. Our CWV sub-group was small (n\u0026thinsp;=\u0026thinsp;10) and comprised relatively healthy adults; therefore, extrapolation to older, frailer, or clinical populations should be made cautiously. The agreement statistics (bias and limits of agreement) should be further validated in larger, heterogeneous samples. Additionally, although we demonstrate cross-sectional agreement, we did not investigate whether walking training prescribed at WV₃min yields better outcomes than standard prescriptions. Finally, some correlations with WV₃min were moderate despite being non-significant, indicating a potential beta error due to the small sample size. There is evidence that non-exhaustive tests can predict CP, race performance, or related physiological/anthropometric measures \u0026mdash; but prediction precision is moderate and depends on the cohort and method [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, although CP may be predicted by non-exhaustive exercise tests [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], apart from lean body mass or muscle girth [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], it has been shown that it is also predicted weekly by non-exercise attributes, such as those investigated in the present study. Non-exercise factors, such as age, sex, body size, and BMI, can influence aerobic capacity and possibly CP; however, large-scale studies reporting simple correlations between CP and these predictors are lacking. Most current research targets physiological factors (VO₂max, W\u0026rsquo;, muscle phenotype), not demographic or anthropometric ones. Future work should validate the findings over time, assess the training effects, and explore the cardiovascular and metabolic implications.\u003c/p\u003e \u003cp\u003eIn summary, we may conclude that an encouraged M6MWT with calculation of WV₃min provides a valid, low-burden method for estimating CWV in asymptomatic adults, enabling the targeted prescription of walking exercise intensity for health-promotion programs. The WV₃min offers a reliable and straightforward estimate of maximal sustainable walking velocity in asymptomatic adults. As it is easily obtained and not strongly influenced by common demographic or cardiovascular risk factors, WV₃min can be used to guide safe and effective walking exercise prescriptions. The accompanying smartphone app offers a practical tool for health professionals to personalize walking intensity, supporting the broader implementation of optimized walking programs in health promotion and primary prevention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWV₃min\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eaverage walking velocity during the last three minutes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM6MWT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emodified six-minute walk test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCWV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecritical walking velocity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e6MWTVel\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall six-minute walk velocity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e6MWT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esix-minute walk test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVO2max\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emaximum oxygen uptake\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecritical power\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISWT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eincremental shuttle walk test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISWD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eincremental shuttle walk distance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e6MWD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esix-minute walk distance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e6MWS App\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSix-minute Walk Speed App\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoefficient of variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Federal University of S\u0026atilde;o Paulo Ethics Committee approved the study (#186.796)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available for the following reasons:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. The are part of a cohort study. There are ongoing analysis being conducted. The results will be submitted for publication shortly and then publicized.\u0026nbsp;However, our data are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTransparency is a cornerstone of our research. We want to assure our readers that none of the authors has any conflict of interest, ensuring the integrity and impartiality of our findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVD had full access to the study\u0026apos;s data and took responsibility for its integrity and accuracy. VZD also conducted data analysis and wrote the first version of the manuscript. TO substantially contributed to the study design and data interpretation. All the authors approved the final version of the manuscript. VD was mainly responsible for the data management in the RedCap platform. Finaly, VD developed the Six-Minute Walk Speed App using Flutter framework and Dart programming language.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Sao Paulo Research Foundation funded this Study, grant #2011/07282-6;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for the financial support that made these studies possible as detailed in the Declarations Section. The Sao Paulo Research Foundation (FAPESP) funded the development of walking tests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMurtagh EM, Nichols L, Mohammed MA et al (2015) The effect of walking on risk factors for cardiovascular disease: An updated systematic review and meta-analysis of randomised control trials. Prev Med (Baltim) ; 72\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiercy KL, Troiano RP (2018) Physical Activity Guidelines for Americans From the US Department of Health and Human Services. 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Eur J Appl Physiol 120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00421-020-04459-6\u003c/span\u003e\u003cspan address=\"10.1007/s00421-020-04459-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Federal University of Sao Paulo","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Critical power, 6MWT, Cardiorespiratory fitness, Exercise, Smartphone, Application","lastPublishedDoi":"10.21203/rs.3.rs-9558754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9558754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eIn asymptomatic adults, we examined whether the average walking velocity during the last three minutes (WV₃min) of an encouraged modified six-minute walk test (M6MWT) could estimate the critical walking velocity (CWV) obtained from high-intensity constant-speed walking tests.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThirty-five adults (60% women; 40\u0026ndash;83 years) participated. Ten completed both the CWV protocol and M6MWT, while twenty-five performed only the M6MWT using a smartphone app to facilitate measurements of walking velocity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the subsample (n\u0026thinsp;=\u0026thinsp;10), mean CWV (1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 m/s) and WV₃min (1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 m/s) did not differ significantly (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), with low variability (CV\u0026thinsp;=\u0026thinsp;8.6%) and minimal bias (\u0026ndash;0.04 m/s; 95% CI \u0026minus;\u0026thinsp;0.34 to +\u0026thinsp;0.41 m/s). In the larger group, WV₃min showed weak, non-significant correlations with age, anthropometric measures, and cardiovascular risk factors; however, it correlated strongly with the overall six-minute walk velocity (6MWTVel) (r\u0026thinsp;=\u0026thinsp;0.83; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). We fitted a regression model to predict WV₃min from 6MWTVel (R\u0026sup2; = 0.689).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe may conclude that WV₃min approximates CWV with acceptable agreement and variability. This approach, supported by a smartphone application, may offer a practical, submaximal alternative for estimating sustainable walking velocity and optimizing exercise prescription in preventive and health-promotion contexts.\u003c/p\u003e","manuscriptTitle":"Smartphone-Based Estimation of Critical Walking Velocity from the Six-Minute Walk Test in Asymptomatic Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 03:52:00","doi":"10.21203/rs.3.rs-9558754/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3654623c-2136-4132-a550-1cc5ade36d37","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67199635,"name":"Physiology"}],"tags":[],"updatedAt":"2026-04-30T03:52:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 03:52:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9558754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9558754","identity":"rs-9558754","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}