Timo Eronen, Jukka A. Lipponen, Vesa Hyrylä, Saana Kupari, Jaakko Mursu, Mika Venojärvi, Heikki O. Tikkanen, Mika P. Tarvainen
{"title":"基于心率变异的通气阈值估算--商用算法的验证","authors":"Timo Eronen, Jukka A. Lipponen, Vesa Hyrylä, Saana Kupari, Jaakko Mursu, Mika Venojärvi, Heikki O. Tikkanen, Mika P. Tarvainen","doi":"10.1101/2024.08.14.24311967","DOIUrl":null,"url":null,"abstract":"Ventilatory thresholds (VT1 and VT2) are critical in exercise prescription and athletic training, delineating the transitions from aerobic to anaerobic metabolism. More specifically, VT1 signifies the onset of lactate accumulation whilst VT2 signifies the onset of metabolic acidosis. Accurate determination of these thresholds is vital for optimizing training intensity. Fractal correlation properties of heart rate variability (HRV), particularly the short-term scaling exponent alpha 1 of Detrended Fluctuation Analysis (DFA-α1), have demonstrated potential for this purpose. This study validates the accuracy of commercial ventilatory threshold estimation algorithm (VT-algorithm) developed by Kubios. The VT-algorithm employs instantaneous heart rate (HR) relative to HR reserve and respiratory rate (RF), along with the DFA-α1. Sixty-four physically active participants underwent an incremental cardiopulmonary exercise test (CPET) with inter-beat interval (RR) measurements. DFA-α1 and the Kubios VT-algorithm were used to assess HR and oxygen uptake (VO2) at ventilatory thresholds. On average VO2 at true VT, DFA-α1, and VT-algorithm derived ventilatory thresholds were 1.74, 2.00 and 1.89 l/min (VT1) and 2.40, 2.41 and 2.40 l/min (VT2), respectively. Correspondingly, average HRs at the true VT, DFA-α1, and VT-algorithm thresholds were 141, 151 and 142 bpm (VT1) and 169, 168 and 170 bpm (VT2), respectively. When compared to the true thresholds, Bland-Altman error statistics (bias ± standard deviation of error) for the DFA-α1 thresholds were -0.26±0.41 l/min or -10±16 bpm at VT1 and 0.00±0.34 l/min or 1±10 bpm at VT2, whereas the VT-algorithm errors were -0.15±0.28 l/min or -1±11 bpm at VT1 and 0.01±0.20 l/min or -1±7 bpm at VT2. HRV based VT determination algorithms accurately estimate ventilatory thresholds, offering insights into training zones, internal loading, and metabolic transitions during exercise without the need of laboratory equipment. The Kubios VT-algorithm, which incorporates instantaneous HR and RF along with DFA-α1, provided higher accuracy for VO2 and HR values for both VT1 and VT2.","PeriodicalId":501122,"journal":{"name":"medRxiv - Sports Medicine","volume":"10 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Heart Rate Variability Based Ventilatory Threshold Estimation - Validation of a Commercially Available Algorithm\",\"authors\":\"Timo Eronen, Jukka A. Lipponen, Vesa Hyrylä, Saana Kupari, Jaakko Mursu, Mika Venojärvi, Heikki O. Tikkanen, Mika P. Tarvainen\",\"doi\":\"10.1101/2024.08.14.24311967\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ventilatory thresholds (VT1 and VT2) are critical in exercise prescription and athletic training, delineating the transitions from aerobic to anaerobic metabolism. More specifically, VT1 signifies the onset of lactate accumulation whilst VT2 signifies the onset of metabolic acidosis. Accurate determination of these thresholds is vital for optimizing training intensity. Fractal correlation properties of heart rate variability (HRV), particularly the short-term scaling exponent alpha 1 of Detrended Fluctuation Analysis (DFA-α1), have demonstrated potential for this purpose. This study validates the accuracy of commercial ventilatory threshold estimation algorithm (VT-algorithm) developed by Kubios. The VT-algorithm employs instantaneous heart rate (HR) relative to HR reserve and respiratory rate (RF), along with the DFA-α1. Sixty-four physically active participants underwent an incremental cardiopulmonary exercise test (CPET) with inter-beat interval (RR) measurements. DFA-α1 and the Kubios VT-algorithm were used to assess HR and oxygen uptake (VO2) at ventilatory thresholds. On average VO2 at true VT, DFA-α1, and VT-algorithm derived ventilatory thresholds were 1.74, 2.00 and 1.89 l/min (VT1) and 2.40, 2.41 and 2.40 l/min (VT2), respectively. Correspondingly, average HRs at the true VT, DFA-α1, and VT-algorithm thresholds were 141, 151 and 142 bpm (VT1) and 169, 168 and 170 bpm (VT2), respectively. When compared to the true thresholds, Bland-Altman error statistics (bias ± standard deviation of error) for the DFA-α1 thresholds were -0.26±0.41 l/min or -10±16 bpm at VT1 and 0.00±0.34 l/min or 1±10 bpm at VT2, whereas the VT-algorithm errors were -0.15±0.28 l/min or -1±11 bpm at VT1 and 0.01±0.20 l/min or -1±7 bpm at VT2. HRV based VT determination algorithms accurately estimate ventilatory thresholds, offering insights into training zones, internal loading, and metabolic transitions during exercise without the need of laboratory equipment. The Kubios VT-algorithm, which incorporates instantaneous HR and RF along with DFA-α1, provided higher accuracy for VO2 and HR values for both VT1 and VT2.\",\"PeriodicalId\":501122,\"journal\":{\"name\":\"medRxiv - Sports Medicine\",\"volume\":\"10 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"medRxiv - Sports Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.08.14.24311967\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Sports Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.08.14.24311967","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Heart Rate Variability Based Ventilatory Threshold Estimation - Validation of a Commercially Available Algorithm
Ventilatory thresholds (VT1 and VT2) are critical in exercise prescription and athletic training, delineating the transitions from aerobic to anaerobic metabolism. More specifically, VT1 signifies the onset of lactate accumulation whilst VT2 signifies the onset of metabolic acidosis. Accurate determination of these thresholds is vital for optimizing training intensity. Fractal correlation properties of heart rate variability (HRV), particularly the short-term scaling exponent alpha 1 of Detrended Fluctuation Analysis (DFA-α1), have demonstrated potential for this purpose. This study validates the accuracy of commercial ventilatory threshold estimation algorithm (VT-algorithm) developed by Kubios. The VT-algorithm employs instantaneous heart rate (HR) relative to HR reserve and respiratory rate (RF), along with the DFA-α1. Sixty-four physically active participants underwent an incremental cardiopulmonary exercise test (CPET) with inter-beat interval (RR) measurements. DFA-α1 and the Kubios VT-algorithm were used to assess HR and oxygen uptake (VO2) at ventilatory thresholds. On average VO2 at true VT, DFA-α1, and VT-algorithm derived ventilatory thresholds were 1.74, 2.00 and 1.89 l/min (VT1) and 2.40, 2.41 and 2.40 l/min (VT2), respectively. Correspondingly, average HRs at the true VT, DFA-α1, and VT-algorithm thresholds were 141, 151 and 142 bpm (VT1) and 169, 168 and 170 bpm (VT2), respectively. When compared to the true thresholds, Bland-Altman error statistics (bias ± standard deviation of error) for the DFA-α1 thresholds were -0.26±0.41 l/min or -10±16 bpm at VT1 and 0.00±0.34 l/min or 1±10 bpm at VT2, whereas the VT-algorithm errors were -0.15±0.28 l/min or -1±11 bpm at VT1 and 0.01±0.20 l/min or -1±7 bpm at VT2. HRV based VT determination algorithms accurately estimate ventilatory thresholds, offering insights into training zones, internal loading, and metabolic transitions during exercise without the need of laboratory equipment. The Kubios VT-algorithm, which incorporates instantaneous HR and RF along with DFA-α1, provided higher accuracy for VO2 and HR values for both VT1 and VT2.