Browsing by Author "Hesse, Anton"

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    Data Processing Methods And Their Effects On The Limits Of Agreement And Reliability Of Automated Submaximal Threshold Calculations
    (2023-09) Hesse, Anton
    Cardiovascular exercise intensity constitutes an important component of exercise prescription, but its individualization poses challenges. Previous research underscores the greater efficacy of physiological threshold-based cardiovascular exercise intensity prescription, when compared to standardized percentages of maximum heart rate, VO2, or workload. However, identifying these thresholds usually entails pre-processing steps like outlier elimination, interpolation, and data averaging. Although diverse algorithms exist to pinpoint these thresholds, the influence of prior data processing steps on algorithm-derived thresholds remains unclear. Through a scoping review, we gathered articles from studies that collected breath-by-breath gas exchange data during exercise in humans. We assessed the reporting prevalence and the nature of outlier removal, interpolation, and data-averaging methods. Approximately 5% of articles described outlier removal and interpolation details in their methods, while 2/3 reported data averaging. We developed an open-source R package, ”gasExchangeR,” to assess the effects of data processing choices on algorithm-derived thresholds. We included multiple threshold-detection algorithms from previous research in this package and validated them against simulated and human exercise tests. Most algorithms performed well under low simulated noise conditions but had higher relative and absolute error than visual detection. Leveraging the gasExchangeR package, algorithm-derived thresholds were computed across varied outlier removal limits, averaging durations, and algorithms using 350 exercise tests. A similar analysis was performed with 17 participants to assess the effect of these parameters on the test-retest reliability of algorithm-derived thresholds. The outcomes exhibited generally negligible main effects and interactions between outlier removal limit, averaging duration, and algorithm selection on average threshold values. Nevertheless, some statistically significant differences were observed. The 95% limits of agreement (LOA) among diverse data processing and algorithm combinations exceeded the expected measurement error in VO2. Linear regression highlighted algorithmic comparisons as the primary contributor to LOA variance. Specific algorithm types yielded statistically significant ICC values more frequently. These findings indicate that manipulating the data appearance despite constant underlying fitness can unveil inherent variability in distinct algorithms. In aggregate, these investigations underscore the potential for enhanced reproducibility through improved method documentation and the use of open-source software.
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    Examining the Respiratory Compensation Point with Automated Methods in Recreational Runners Training for a Marathon
    (2019-05) Hesse, Anton
    Background: The respiratory compensation point (RC) approximates the lowest intensity of unsustainably difficult exercise, making it an important measure for endurance athletes. Thus, accurate determination of RC is important to athletes. There are many methods to determine RC, but few large studies to date have compared multiple automated methods. Previous studies have shown that rates of detection of RC (i.e. determinate cases) vary. The purpose of this study was to compare four common methods used to detect RC: Jones-Molitoris (JM), Orr, Beaver’s V-slope (Beaver), and the Dmax method. Methods: Recreationally active college students (n = 131, 45 males, 86 females) completed 2-mile time trials and graded exercise tests both before and after training for a marathon. The four methods were used to detect RC (as a % of VO2max) from the VE vs. VCO2 slope. The number of determinate RC cases were recorded for each method at pre and at post. Determinate counts of RC were expressed as a percentage, were compared pre to post with Fisher’s exact tests, and were simulated with bootstrap resampling. Average differences between methods were compare using a linear mixed effects model (LMEM) with data from participants who displayed RC at both pre and post testing for at least one of the four methods. Comparisons between methods and with 2-mile performance were also compared by correlations and with limits of agreement (LOA) plots. Results: The order of determinate rates from highest to lowest was JM, Dmax, Orr, and Beaver. Fisher’s exact tests produced odds ratios significantly higher than 1 for all but Beaver. Histograms of bootstrap resampling showed large overlap for all but the Beaver method. LMEM analysis showed that JM predicted significantly higher RC than Beaver and Dmax, but not Orr. All methods were significantly correlated with one another at both timepoints. LOA were wide. Conclusions: Beaver detects RC more infrequently than other methods. It is unknown if the higher %VO2max at RC predicted by JM is an overestimate. Although all methods highly and significantly correlate to one another, they have wide LOA. A better automated method may combine the results of several methods.