VT1 matters because it is a non-invasive, effort-independent window into aerobic fitness. Unlike peak oxygen uptake, which depends on whether a person truly pushes to their limit, VT1 emerges from underlying physiology: as exercise intensity rises, lactate is buffered by bicarbonate, producing extra carbon dioxide. This causes carbon dioxide output to climb disproportionately relative to oxygen uptake, and ventilation follows. The classic V-slope method, introduced by Beaver, Wasserman and Whipp in 1985, detects the breakpoint in the relationship between carbon dioxide output and oxygen uptake. Exercise prescriptions built around VT1 have been shown to deliver greater improvements in peak oxygen uptake and metabolic health than prescriptions based on fixed percentages of maximum capacity, making accurate threshold detection far more than an academic exercise.

The problem has always been that finding that breakpoint is largely an art. Trained physicians inspect the plotted curve and mark where the slope changes, often cross-checking with complementary techniques such as the ventilatory equivalents or end-tidal pressure methods. Even among experienced assessors, disagreement occurs in roughly 39 percent of cases, with variability of about 11 percent in healthy individuals and up to 18 percent among less experienced raters. Visual assessment is also slow, a serious bottleneck when large datasets from thousands of tests must be analysed. Automated detection promises objectivity, reproducibility and speed, but earlier attempts have struggled: a software tool tested in more than 500 asymptomatic volunteers produced mean differences of 22 percent with limits of agreement exceeding 800 mL/min, while a convolutional neural network approach reported average differences of 11 percent.

The Belgian team, led by Margot Vermeiren, set out to validate a revised V-slope algorithm that builds on the original Beaver approach but replaces its iterative breakpoint search with a fully data-driven mathematical procedure. The algorithm fits both a linear regression and a quadratic curve to the carbon dioxide-oxygen relationship across the incremental exercise phase, then locates the point of maximum perpendicular distance between the two fits. That point divides the data into lower and upper segments, each fitted with its own linear regression. The breakpoint is accepted as VT1 only if the difference in slope between the two segments exceeds 0.1, a criterion inherited from the original method to filter out spurious breakpoints caused by noise. If the criterion is not met, the algorithm is allowed to examine neighbouring values before giving up.

To test the algorithm, the researchers drew on the iCOMPEER registry, a retrospective collection of cardiopulmonary exercise tests performed on a cycle ergometer at University Hospitals Leuven between 2010 and 2020. From 3,466 individuals, strict exclusions removed anyone with cardiovascular, pulmonary or autoimmune disease, diabetes, malignancy or bariatric surgery, along with users of certain medications, people aged 80 or older, and those with a body mass index below 18. Of 813 eligible subjects, 271 had raw time-series data available, and after removing tests with indeterminate or failed assessments the final cohort comprised 262 adults, with a mean age of 46 years, 61 percent women, predominantly overweight, and a good average exercise capacity at 93 percent of predicted maximum. Both the manual and automated assessments were applied to identically pre-processed signals, with outliers corrected using a custom Python pipeline.

The reference standard was deliberately rigorous. Two experienced observers independently inspected every curve and marked the visual V-slope breakpoint, and their average value became the manual VT1. Tests in which no discernible breakpoint existed were classified as indeterminate and excluded when both observers agreed. The automated method, implemented through the publicly available get_GET package in Python, then processed the same data. The team compared the two approaches using a battery of complementary statistics: Bland-Altman analysis for bias and limits of agreement, intraclass correlation coefficients for agreement, Deming regression to account for error in both methods, and formal equivalence testing with pre-defined margins.

The headline result is striking. Median oxygen uptake at VT1 was 1,151 mL/min with the algorithm versus 1,122 mL/min by eye, a mean difference of just 29 mL/min, or 2.2 percent, with limits of agreement of minus 11.7 to plus 16.0 percent after adjusting for the fact that measurement error grows with the magnitude of the measurement. The intraclass correlation coefficient reached 0.97, and Deming regression yielded a slope of 0.95 with an intercept of 26.1 mL/min, indicating a small proportional bias whereby automated values ran slightly higher at greater fitness levels. Equivalence testing with a margin of 100 mL/min was significant, confirming that the methods agree within a clinically meaningful band. Crucially, the inter-method agreement of 2.2 percent closely approached the 0 to 11 percent variability reported between experienced cardiologists, and the algorithm’s ICC of 0.97 actually exceeded the 0.93 agreement between the two human raters themselves.

The picture is less tidy at the level of the individual. The absolute limits of agreement, spanning roughly minus 140 to plus 199 mL/min, exceeded the pre-specified 100 mL/min boundary, and 21 percent of participants fell outside that margin. Agreement was better in women than in men, likely reflecting finer physiological resolution from the smaller work-rate increments used in their protocols. The algorithm also failed to identify a valid threshold in about 2 percent of tests, most of which were also indeterminate by eye, suggesting that these failures reflect genuinely ambiguous curve shapes rather than a specific weakness of the software. The researchers catalogued several sources of discordance: near-linear curves without a clear breakpoint, coarse data resolution near the threshold, gradual transitions where the eye picks the first deviation while the algorithm anchors to maximum curvature, artefactual data points that distort regression fitting, and early thresholds that leave an excess of high-intensity data points pulling the mathematical breakpoint rightward.

Heart rate at VT1, often used to translate the threshold into training zones, told a similar story. Agreement was good, with an ICC of 0.91, a mean bias of 4 beats per minute, and formal equivalence within a 7 bpm margin, though the sex pattern reversed, with marginally better agreement in men, possibly because women’s smaller stroke volumes demand steeper heart rate responses that amplify small threshold discrepancies.

The authors conclude that the revised V-slope algorithm is a reliable, standardized and time-efficient tool for large-scale research applications in healthy adults, where modest individual disagreement can be tolerated and expert availability is limited. But they are careful about the clinic: because individual estimates may differ meaningfully from expert assessment in a non-negligible share of cases, automated VT1 values should be reviewed by an experienced assessor before guiding exercise prescription for a specific patient. The study population was also exclusively healthy, so validation in people with heart failure, lung disease or metabolic disorders, where ventilatory responses are more complex, remains an essential next step. Still, with the algorithm and data-cleaning code openly available on GitHub, the work marks a significant step toward making one of exercise science’s most valuable measurements faster, more consistent, and accessible at scale.

Subject of Research: Validation of an automated V-slope algorithm for detecting the first ventilatory threshold during cardiopulmonary exercise testing in healthy adults

Article Title: Validation of a Revised V-Slope Algorithm for Automated First Ventilatory Threshold Detection in Healthy Adults

Article References: Vermeiren, M., Goetschalckx, K., Van Opstal, H., Claes, J., Van Langenhoven, L., Cauwenberghs, N., Willems, R., Ntalianis, E., Kuznetsova, T., Cornelissen, V., & Sinnaeve, P. (2026). Validation of a Revised V-Slope Algorithm for Automated First Ventilatory Threshold Detection in Healthy Adults. Sports Medicine – Open, 12(1), Article 153. https://doi.org/10.1186/s40798-026-01124-8

Image Credits: AI Generated

DOI: 10.1186/s40798-026-01124-8

Keywords: cardiopulmonary exercise testing, ventilatory threshold, V-slope method, aerobic fitness, exercise prescription, algorithm validation, Bland-Altman analysis, oxygen uptake, rehabilitation, sports medicine, Validation, Revised

The study drew on the COmPLETE cohort, a cross-sectional investigation conducted at the University of Basel in Switzerland in collaboration with the Royal Brompton Hospital in London. All participants were free from cardiopulmonary disease, screened for active respiratory infection, and assessed with spirometry interpreted according to European Respiratory Society and American Thoracic Society guidelines using race-neutral Global Lung Initiative reference values. Each volunteer then completed a maximal cardiopulmonary exercise test on a cycle ergometer, with ventilation measured on a breath-by-breath basis and averaged over 10-second intervals. The cohort was remarkably balanced: women made up 52 percent of participants, body mass index averaged 23.7 kilograms per square meter, and lung function sat comfortably within normal limits, with predicted forced expiratory volume in one second at 107 percent and predicted forced vital capacity at 110 percent. In other words, these were genuinely healthy adults, not patients, which makes the age-related changes all the more telling.

The researchers framed their investigation around a growing appreciation in physiology that the irregularity, or complexity, of a biological signal can itself be a marker of health. In cardiology, reduced heart rate variability is a well-established signal of autonomic dysfunction and disease risk. But the application of entropy analysis to breathing during exercise has been far more limited. Sample entropy works by asking how predictable a time series is: a highly repetitive, regular pattern scores low, while a complex, irregular pattern scores high. The team calculated sample entropy for minute ventilation, breathing frequency, and tidal volume in every participant, using an embedding dimension of two and a tolerance coefficient of 0.2. Their initial hypothesis, modeled on the heart rate variability literature, was that aging would reduce ventilatory irregularity, constraining the breathing pattern in older adults. The data told a different story.

First, the conventional measures. As expected, exercise capacity declined steadily with age. Peak power output, peak oxygen uptake, peak minute ventilation, peak tidal volume, and peak breathing frequency all fell progressively across the decades, with correlations ranging from modest to strong. Peak oxygen uptake dropped from roughly 45 milliliters per kilogram per minute in the twenties to under 23 in the oldest group. But the striking finding was the shape of the change in breathing strategy. At the first ventilatory threshold, breathing frequency remained essentially flat across the entire age range, hovering around 20 to 22 breaths per minute regardless of whether a participant was 25 or 85. Yet the ratio of breathing frequency to minute ventilation traced a non-linear curve: stable from age 20 to about 55, then inflecting sharply upward. At peak exercise the same pattern emerged, with the frequency-to-ventilation ratio rising markedly in the 70-to-79 and 80-plus decades, indicating a shift toward a tachypneic, or rapid-and-shallow, breathing strategy.

This shift makes mechanical sense. Healthy aging is associated with loss of lung elastic recoil, stiffening of the chest wall, reduced respiratory muscle strength, and declining tidal volumes relative to lung capacity. As the lungs lose their spring, breathing deep becomes expensive. Older adults compensate by breathing more often rather than more deeply, achieving the required ventilation through frequency rather than depth. The study found that the tidal volume to forced vital capacity ratio rose with age at both threshold and peak exercise, confirming that older breathers were working closer to their volumetric limits. Age and peak breathing frequency emerged as independent predictors of ventilatory reserve utilization, the percentage of estimated maximal voluntary ventilation consumed during exercise. Meanwhile ventilatory efficiency deteriorated, with the ventilatory equivalent for carbon dioxide nadir climbing from 24 in the youngest group to 32 in those over 80, meaning older adults had to move substantially more air to eliminate the same amount of carbon dioxide.

Then came the entropy results, which overturned the researchers’ expectations. After adjusting for sex, body mass index, peak oxygen uptake, forced expiratory volume in one second, and exercise duration, generalized additive models revealed a non-linear relationship between age and ventilatory sample entropy across all three signals. Entropy values held steady through early and middle adulthood, then climbed progressively from approximately age 60 onward. The pattern appeared in minute ventilation entropy, tidal volume entropy, and breathing frequency entropy, with the models for minute ventilation explaining just over a quarter of the deviance. Far from becoming more rigid and predictable, the breathing of older adults became demonstrably more chaotic during exercise. The team had hypothesized a decline in irregularity with age, mirroring the well-documented decline in heart rate variability, and the opposite occurred.

The authors offer several plausible mechanisms, while cautioning that the observational design cannot settle the question definitively. One possibility is altered central respiratory drive or impaired integration of afferent feedback within the brainstem, a form of neural dedifferentiation consistent with the broader concept of loss of physiological complexity in aging. Elevated entropy has previously been documented in dysfunctional breathing and in post-COVID unexplained breathlessness, conditions where patients report exertional breathlessness without any underlying cardiorespiratory pathology. Intriguingly, the age-related rise in entropy coincides with the period when around a quarter of people over 65 report breathlessness in the absence of diagnosed disease. The stability of breathing frequency at the first ventilatory threshold suggests that at submaximal intensities, age-related mechanical constraints have not yet compromised ventilation; the irregularity and tachypneic shift become evident only as exercise intensity climbs into higher domains. Sex differences added another layer: males showed higher minute ventilation entropy than females, possibly because larger lung volumes and airway dimensions in males produce a broader, less constrained ventilatory response that registers as a more irregular time series.

The study also connected ventilatory irregularity to fitness. In secondary analyses, higher peak oxygen uptake was associated with lower sample entropy across all three ventilatory signals, and peak oxygen uptake was the primary determinant of minute ventilation and frequency entropy in models adjusted for age and body mass index. The authors are careful to note that this may reflect shared age-related influences rather than a direct mechanistic relationship, since cardiorespiratory fitness declines steadily after the fifth decade, roughly the same point at which entropy begins its ascent. Still, the possibility that fitness protects the fine-grained organization of breathing control is a tantalizing lead. The findings also fit into a developmental arc suggested by prior work in children and adolescents, where entropy values decline during maturation, indicating increasing refinement of respiratory control. Taken together, these studies sketch a lifespan trajectory in which breathing control is refined through childhood and adolescence, stabilizes through adulthood, and then de-differentiates in later life, echoing patterns observed in frailty research across multiple physiological systems.

Limitations are candidly acknowledged. The analysis was retrospective and cross-sectional, so longitudinal data would be needed to track how individual breathing patterns evolve with age. All testing occurred at a single Swiss center in a predominantly Caucasian cohort, which limits generalizability, and no perceptual breathlessness ratings such as Borg scale scores were collected, preventing a direct link between entropy and symptom intensity in this cohort. The use of estimated rather than directly measured maximal voluntary ventilation is a further caveat, though the two are strongly correlated in healthy people. Crucially, the proportion of the exercise test spent above the first ventilatory threshold was comparable across all age decades, between 52 and 54 percent, suggesting that differences in relative exercise intensity did not confound the entropy findings. The authors also provide normative entropy values across the age span, a reference standard intended to anchor future studies of breathlessness in older adults.

The bigger picture is compelling: the study demonstrates that the aged respiratory system is forced into a mechanically inefficient, frequency-dominant pattern accompanied by rising signal irregularity, a kind of neuromechanical dissociation that may underpin the heightened perception of breathlessness so commonly reported with healthy aging. Because breathing frequency tracks perceived exertion closely, the tachypneic shift documented here provides a physiological bridge between the aging lung and the everyday experience of getting winded. Future prospective studies correlating entropy with breathlessness scores, and longitudinal designs tracking entropy within individuals over time, will be needed to convert these cross-sectional patterns into clinical insight. But the message already resonates: aging does not simply dim the respiratory system, it rewires its control, and the mathematical fingerprint of that rewiring becomes visible somewhere around our sixtieth birthday.

Subject of Research: Age-related changes in ventilatory pattern and irregularity during exercise in healthy adults

Article Title: The ventilatory response pattern to exercise across the age‐span in healthy adults

Article References: Williams, Z. J., Cenerini, G., Schwendinger, F., Knaier, R., Wagner, J., Hull, J. H., & Schmidt‐Trucksäss, A. (2026). The ventilatory response pattern to exercise across the age‐span in healthy adults. Physiological Reports, 14(17), Article e71065. https://doi.org/10.14814/phy2.71065

Image Credits: AI Generated

DOI: 10.14814/phy2.71065

Keywords: healthy aging, ventilatory response, sample entropy, cardiopulmonary exercise testing, breathing frequency, tidal volume, exertional breathlessness, respiratory mechanics, physiological complexity, cardiorespiratory fitness, ventilatory threshold, COmPLETE study